Keith Price Bibliography Number of Features, Dimensionality, Dimensionality Reduction

14.1.3.2 Number of Features, Dimensionality, Dimensionality Reduction

Chapter Contents (Back)
Dimensionality. Dimensionality Reduction. See also Computation and Analysis of Principal Components, Eigen Values, SVD. See also Hyperspectral Data, Dimensionality Reduction.

Fukunaga, K., and Olsen, D.R.,
An Algorithm for Finding Intrinsic Dimensionality of Data,
TC(20), No. 2, 1971, pp. 176-183. Cubic time complexity with respect to dimensionality of input space. See also Application of the Karhunen-Loeve Expansion to Feature Selection and Ordering. BibRef 7100

Koontz, W.L.G., and Fukunaga, K.,
A Nonparametric Valley-Seeking Technique for Cluster Analysis,
TC(21), 1972, pp. 171-178. BibRef 7200

Koontz, W.L.G., and Fukunaga, K.,
Asymptotic Analysis of a Nonparametric Clustering Technique,
TC(21), 1972, pp. 967-974. BibRef 7200

Koontz, W.L.G., Narendra, P.M., and Fukunaga, K.,
A Graph-Theoretic Approach to Nonparametric Cluster Analysis,
TC(25), 1976, pp. 936-944. BibRef 7600

Raudys, S.J.[Sarunas J.],
Determination of optimal dimensionality in statistical pattern classification,
PR(11), No. 4, 1979, pp. 263-270.
WWW Version. 0309
BibRef

Raudys, S.J.[Sarunas J.], Pikelis, V.,
On Dimensionality, Sample Size, Classification Error, and Complexity of Classification Algorithms in Pattern Recognition,
PAMI(2), No. 3, May 1980, pp. 243-252. Dimensionality analysis. BibRef 8005

Raudys, S.J.[Sarunas J.],
Feature Over-Selection,
SSPR06(622-631).
Springer DOI Link 0608
BibRef

Schwartz, G.,
Estimating the Dimension of a Model,
AMS(6), 1978, pp. 461-464. Bayesian information criterion. BibRef 7800

Jain, A.K., Waller, W.G.,
On the optimal number of features in the classification of multivariate Gaussian data,
PR(10), No. 5-6, 1978, pp. 365-374.
WWW Version. 0309
BibRef

Trunk, G.V.,
Range Resolution of Targets using Automatic Detection,
AeroSys(14), No. 5, 1978, pp. 750-755. BibRef 7800

Trunk, G.V.,
A Problem of Dimensionality: A Simple Example,
PAMI(1), No. 3, July 1979, 306-307. BibRef 7907

Odell, P.L.[Patrick L.],
A model for dimension reduction in pattern recognition using continuous data,
PR(11), No. 1, 1979, pp. 51-54.
WWW Version. 0309
BibRef

El-Sheikh, T.S., Wacker, A.G.,
Effect of dimensionality and estimation on the performance of gaussian classifiers,
PR(12), No. 3, 1980, pp. 115-126.
WWW Version. 0309
BibRef

Kakusho, O.[Osamu], Mizoguchi, R.[Riichiro],
A new algorithm for non-linear mapping with applications to dimension and cluster analyses,
PR(16), No. 1, 1983, pp. 109-117.
WWW Version. 0309
BibRef

Saund, E.,
Dimensionality-reduction using connectionist networks,
PAMI(11), No. 3, March 1989, pp. 304-314.
IEEE Abstract.
WWW Version. 0401
BibRef

Buturovic, L.J.,
Toward Bayes-optimal linear dimension reduction,
PAMI(16), No. 4, April 1994, pp. 420-424.
IEEE Abstract.
WWW Version. 0401
BibRef
Earlier:
Near-optimal algorithm for dimension reduction,
ICPR92(II:401-404).
IEEE DOI Link 9208
BibRef

Verveer, P.J., Duin, R.P.W.,
An Evaluation of Intrinsic Dimensionality Estimators,
PAMI(17), No. 1, January 1995, pp. 81-86.
IEEE Abstract.
WWW Version. BibRef 9501

Bruske, J., Sommer, G.,
Intrinsic Dimensionality Estimation with Optimally Topology Preserving Maps,
PAMI(20), No. 5, May 1998, pp. 572-575.
IEEE Abstract.
WWW Version. 9806
BibRef
Earlier:
An algorithm for intrinsic dimensionality estimation,
CAIP97(9-16).
WWW Version. 9709
Linear time complexity. BibRef

de Backer, S., Naud, A., Scheunders, P.,
Nonlinear Dimensionality Reduction Techniques for Unsupervised Feature Extraction,
PRL(19), No. 8, June 1998, pp. 711-720. 9808
BibRef

Heylen, R.[Rob], Scheunders, P.[Paul],
Nonlinear barycentric dimensionality reduction,
ICIP10(1341-1344).
IEEE DOI Link 1009
BibRef

Roweis, S.T., Saul, L.K.,
Nonlinear Dimensionality Reduction by Locally Linear Embedding,
Science(290), No. 5500, December 2000, pp. 2323-2326.
WWW Version. BibRef 0012

Seung, H.S.[H. Sebastian], and Lee, D.D.[Daniel D.],
The manifold ways of perception,
Science(290), No. 5500, December 2000, pp. 2268-2269.
WWW Version. BibRef 0012

Lotlikar, R.[Rohit], Kothari, R.[Ravi],
Adaptive linear dimensionality reduction for classification,
PR(33), No. 2, February 2000, pp. 185-194.
WWW Version. 0001
BibRef

Lotlikar, R.[Rohit], Kothari, R.[Ravi],
Fractional-Step Dimensionality Reduction,
PAMI(22), No. 6, June 2000, pp. 623-627.
IEEE Abstract.
WWW Version. 0008
BibRef

Tenenbaum, J.B., de Silva, V., and Langford, J.C.,
A Global Geometric Framework for Nonlinear Dimensionality Reduction,
Science(290), No. 5500, 22 December 2000, pp. 2319-2323.
WWW Version.
WWW Version. BibRef 0012

Loog, M.[Marco], Duin, R.P.W., Haeb-Umbach, R.,
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria,
PAMI(23), No. 7, July 2001, pp. 762-766.
IEEE Abstract.
WWW Version. 0108
BibRef
Earlier: A2, A1, A3:
Multi-class Linear Feature Extraction by Nonlinear PCA,
ICPR00(Vol II: 398-401).
IEEE DOI Link 0009
BibRef

Loog, M.[Marco], Duin, R.P.W.[Robert P.W.],
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion,
PAMI(26), No. 6, June 2004, pp. 732-739.
IEEE Abstract. 0404
BibRef

Loog, M.[Marco], van Ginneken, B.[Bram], Duin, R.P.W.[Robert P.W.],
Dimensionality reduction of image features using the canonical contextual correlation projection,
PR(38), No. 12, December 2005, pp. 2409-2418.
WWW Version. 0510
BibRef
Earlier:
Dimensionality Reduction by Canonical Contextual Correlation Projections,
ECCV04(Vol I: 562-573).
WWW Version. 0405
BibRef

Loog, M.[Marco],
On an alternative formulation of the Fisher criterion that overcomes the small sample problem,
PR(40), No. 6, June 2007, pp. 1753-1755.
WWW Version. 0704
BibRef
Earlier:
Conditional Linear Discriminant Analysis,
ICPR06(II: 387-390).
WWW Version. 0609
Fisher criterion; Feature extraction; Small sample problem; Counterexample BibRef

Loog, M.[Marco], de Ridder, D.[Dick],
Local Discriminant Analysis,
ICPR06(III: 328-331).
WWW Version. 0609
BibRef

Qin, A.K., Suganthan, P.N., Loog, M.,
Efficient Feature Extraction Based on Regularized Uncorrelated Chernoff Discriminant Analysis,
ICPR06(III: 125-128).
WWW Version. 0609
BibRef

Cappelli, R.[Raffaele], Maio, D.[Dario], Maltoni, D.[Davide],
Multispace KL for Pattern Representation and Classification,
PAMI(23), No. 9, September 2001, pp. 977-996.
IEEE Abstract.
WWW Version. 0110
For unsupervised dimensionality reduction. BibRef

Camastra, F.[Francesco], Vinciarelli, A.[Alessandro],
Estimating the Intrinsic Dimension of Data with a Fractal-Based Method,
PAMI(24), No. 10, October 2002, pp. 1404-1407.
IEEE Abstract. 0210
BibRef

Camastra, F.[Francesco],
Data dimensionality estimation methods: a survey,
PR(36), No. 12, December 2003, pp. 2945-2954.
WWW Version. 0310
Survey, Dimensionality. BibRef

Wang, X.C.[Xue-Chuan], Paliwal, K.K.[Kuldip K.],
Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition,
PR(36), No. 10, October 2003, pp. 2429-2439.
WWW Version. 0308
BibRef

Yang, L.[Li],
Distance-preserving mapping of patterns to 3-space,
PRL(25), No. 1, January 2004, pp. 119-128.
WWW Version. 0311
Each point is mapped so that its distances to three already mapped points are preserved. BibRef

Yang, L.[Li],
Distance-preserving projection of high dimensional data,
PRL(25), No. 2, January 2004, pp. 259-266.
WWW Version. 0401
BibRef

Zhao, D.F.[Dong-Fang], Yang, L.[Li],
Incremental Construction of Neighborhood Graphs for Nonlinear Dimensionality Reduction,
ICPR06(III: 177-180).
WWW Version. 0609
BibRef

Yang, L.[Li],
Distance-Preserving Projection of High-Dimensional Data for Nonlinear Dimensionality Reduction,
PAMI(26), No. 9, September 2004, pp. 1243-1246.
IEEE Abstract. 0409
BibRef
And:
Locally Multidimensional Scaling for Nonlinear Dimensionality Reduction,
ICPR06(IV: 202-205).
WWW Version. 0609
BibRef

Yang, L.[Li],
Alignment of Overlapping Locally Scaled Patches for Multidimensional Scaling and Dimensionality Reduction,
PAMI(30), No. 3, March 2008, pp. 438-450.
IEEE DOI Link 0801
BibRef

Rangarajan, L.[Lalitha], Nagabhushan, P.,
Dimensionality reduction of multidimensional temporal data through regression,
PRL(25), No. 8, June 2004, pp. 899-910.
WWW Version. 0405
BibRef

Bell, I.E., Baranoski, G.V.G.,
Reducing the dimensionality of plant spectral databases,
GeoRS(42), No. 3, March 2004, pp. 570-576.
IEEE Abstract. 0407
BibRef

Choi, S.J.[Seung-Jin],
Sequential EM learning for subspace analysis,
PRL(25), No. 14, 15 October 2004, pp. 1559-1567.
WWW Version. 0410
PCA for sub space analysis. BibRef

Rangarajan, L.[Lalitha], Nagabhushan, P.,
Content driven dimensionality reduction at block level in the design of an efficient classifier for spatial multi spectral images,
PRL(25), No. 16, December 2004, pp. 1833-1844.
WWW Version. 0411
BibRef

Sun, Q.S.[Quan-Sen], Liu, Z.D.[Zheng-Dong], Heng, P.A.[Pheng-Ann], Xia, D.S.[De-Sen],
A theorem on the generalized canonical projective vectors,
PR(38), No. 3, March 2005, pp. 449-452.
WWW Version. 0412
Feature fusion for character recognition. BibRef

Donoho, D.L.[David L.], Grimes, C.[Carrie],
Image Manifolds which are Isometric to Euclidean Space,
JMIV(23), No. 1, July 2005, pp. 5-24.
Springer DOI Link 0505
Analysis of ISOMap classification. ( See also Global Geometric Framework for Nonlinear Dimensionality Reduction, A. ) BibRef

Kouropteva, O.[Olga], Okun, O.[Oleg], Pietikäinen, M.[Matti],
Incremental locally linear embedding,
PR(38), No. 10, October 2005, pp. 1764-1767.
WWW Version. 0508
BibRef
Earlier:
Incremental Locally Linear Embedding Algorithm,
SCIA05(521-530).
Springer DOI Link 0506
BibRef

Hadid, A., Kouropteva, O., Pietikanen, M.,
Unsupervised Learning Using Locally Linear Embedding: Experiments with Face Pose Analysis,
ICPR02(I: 111-114).
IEEE DOI Link 0211
BibRef

Benito, M.[Monica], Pena, D.[Daniel],
A fast approach for dimensionality reduction with image data,
PR(38), No. 12, December 2005, pp. 2400-2408.
WWW Version. 0510
BibRef

Geng, X., Zhan, D.C., Zhou, Z.H.,
Supervised Nonlinear Dimensionality Reduction for Visualization and Classification,
SMC-B(35), No. 6, December 2005, pp. 1098-1107.
IEEE DOI Link 0512
BibRef

Zhang, K., Chan, L.W.,
Dimension Reduction as a Deflation Method in ICA,
SPLetters(13), No. 1, January 2006, pp. 45-48.
IEEE DOI Link 0601
BibRef

Hsieh, P.F.[Pi-Fuei], Wang, D.S.[Deng-Shiang], Hsu, C.W.[Chia-Wei],
A Linear Feature Extraction for Multiclass Classification Problems Based on Class Mean and Covariance Discriminant Information,
PAMI(28), No. 2, February 2006, pp. 223-235.
IEEE DOI Link 0601
Use pariwise accuracy criterion rather than LDA for dimensionality reduction. BibRef

Law, M.H.C.[Martin H.C.], Jain, A.K.[Anil K.],
Incremental Nonlinear Dimensionality Reduction by Manifold Learning,
PAMI(28), No. 3, March 2006, pp. 377-391.
IEEE DOI Link 0602
BibRef

Hu, Q.H.[Qing-Hua], Yu, D.R.[Da-Ren], Xie, Z.X.[Zong-Xia],
Information-preserving hybrid data reduction based on fuzzy-rough techniques,
PRL(27), No. 5, 1 April 2006, pp. 414-423.
WWW Version. 0604
Attribute reduction; Hybrid data; Fuzzy-rough set; Information measure BibRef

Hu, Q.H.[Qing-Hua], Xie, Z.X.[Zong-Xia], Yu, D.R.[Da-Ren],
Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation,
PR(40), No. 12, December 2007, pp. 3509-3521.
WWW Version. 0709
Numerical feature; Categorical feature; Feature selection; Attribute reduction; Fuzzy set; Rough set; Inclusion degree BibRef

Zhao, D.L.[De-Li],
Formulating LLE using alignment technique,
PR(39), No. 11, November 2006, pp. 2233-2235.
WWW Version. 0608
LLE; LTSA; Nonlinear dimensionality reduction; Manifold learning BibRef

Zheng, Z.L.[Zhong-Long], Yang, J.[Jie],
Supervised locality pursuit embedding for pattern classification,
IVC(24), No. 8, August 2006, pp. 819-826.
WWW Version. 0608
Dimensionality reduction; Principal component analysis; Linear discriminant analysis; Locality pursuit embedding; Supervised learning methods BibRef

Lafon, S.[Stephane], Lee, A.B.[Ann B.],
Diffusion Maps and Coarse-Graining: A Unified Framework for Dimensionality Reduction, Graph Partitioning, and Data Set Parameterization,
PAMI(28), No. 9, September 2006, pp. 1393-1403.
IEEE DOI Link 0608
BibRef

Yan, S.C.[Shui-Cheng], Xu, D.[Dong], Zhang, B.Y.[Ben-Yu], Zhang, H.J.[Hong-Jiang], Yang, Q.A.[Qi-Ang], Lin, S.,
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction,
PAMI(29), No. 1, January 2007, pp. 40-51.
IEEE DOI Link 0701
Graph embedding formulation to unify various dimensionality reduction techniques. An intrinsic graph and a penalty graph to implement Marginal Fisher Analysis. Overcome limitations of LDA. BibRef

Yan, S.C.[Shui-Cheng], Xu, D.[Dong], Zhang, B.Y.[Ben-Yu], Zhang, H.J.[Hong-Jiang],
Graph Embedding: A General Framework for Dimensionality Reduction,
CVPR05(II: 830-837).
IEEE DOI Link 0507
BibRef

Wei, H.L.[Hua-Liang], Billings, S.A.,
Feature Subset Selection and Ranking for Data Dimensionality Reduction,
PAMI(29), No. 1, January 2007, pp. 162-166.
IEEE DOI Link 0701
Forward Orthogonal Search. Select features 1 at a time. BibRef

Zubko, V., Kaufman, Y.J., Burg, R.I., Martins, J.V.,
Principal Component Analysis of Remote Sensing of Aerosols Over Oceans,
GeoRS(45), No. 3, March 2007, pp. 730-745.
IEEE DOI Link 0703
BibRef

Yu, J., Tian, Q., Rui, T., Huang, T.S.,
Integrating Discriminant and Descriptive Information for Dimension Reduction and Classification,
CirSysVideo(17), No. 3, March 2007, pp. 372-377.
IEEE DOI Link 0703
BibRef

Kokiopoulou, E.[Effrosyni], Saad, Y.[Yousef],
Orthogonal Neighborhood Preserving Projections: A Projection-Based Dimensionality Reduction Technique,
PAMI(29), No. 12, December 2007, pp. 2143-2156.
IEEE DOI Link 0711
BibRef

Kokiopoulou, E.[Effrosyni], Saad, Y.[Yousef],
Enhanced graph-based dimensionality reduction with repulsion Laplaceans,
PR(42), No. 11, November 2009, pp. 2392-2402.
Elsevier DOI Link
WWW Version. 0907
Linear dimensionality reduction; Orthogonal projections; Supervised learning; Face recognition; Graph Laplacean BibRef

Fu, Y.[Yun], Huang, T.S.[Thomas S.],
Image Classification Using Correlation Tensor Analysis,
IP(17), No. 2, February 2008, pp. 226-234.
IEEE DOI Link 0801
Correlation-based similarity metric in supervised multilinear discriminant subspace learning can improve classification performance. BibRef

Fu, Y.[Yun], Yan, S.C.[Shui-Cheng], Huang, T.S.[Thomas S.],
Correlation Metric for Generalized Feature Extraction,
PAMI(30), No. 12, December 2008, pp. 2229-2235.
IEEE DOI Link 0811
Alternative to PCA BibRef

Yang, J., Yan, S.C.[Shui-Cheng], Huang, T.S.[Thomas S.],
Ubiquitously Supervised Subspace Learning,
IP(18), No. 2, February 2009, pp. 241-249.
IEEE DOI Link 0901
BibRef

Fu, Y.[Yun], Liu, M.[Ming], Huang, T.S.[Thomas S.],
Conformal Embedding Analysis with Local Graph Modeling on the Unit Hypersphere,
ComponentAnalysis07(1-6).
IEEE DOI Link 0706
project high dimensional data on unit sphere, maintain neighbor relations. BibRef

Sanguinetti, G.[Guido],
Dimensionality Reduction of Clustered Data Sets,
PAMI(30), No. 3, March 2008, pp. 535-540.
IEEE DOI Link 0801
BibRef

Xue, H.[Hui], Chen, S.C.[Song-Can], Zeng, X.Q.[Xiao-Qin],
Classifier learning with a new locality regularization method,
PR(41), No. 5, May 2008, pp. 1496-1507.
WWW Version. 0711
Localized generalization error model; Stochastic sensitivity measure; Locality regularization (LR); Classifier Learning; Pattern classification BibRef

Lin, T.[Tong], Zha, H.B.[Hong-Bin],
Riemannian Manifold Learning,
PAMI(30), No. 5, May 2008, pp. 796-809.
IEEE DOI Link 0803
BibRef

Lin, T.[Tong], Zha, H.B.[Hong-Bin], Lee, S.U.[Sang Uk],
Riemannian Manifold Learning for Nonlinear Dimensionality Reduction,
ECCV06(I: 44-55).
Springer DOI Link 0608
BibRef

Park, C.H.[Cheong Hee], Lee, M.H.[Moon-Hwi],
On applying linear discriminant analysis for multi-labeled problems,
PRL(29), No. 7, 1 May 2008, pp. 878-887.
WWW Version. 0804
Dimension reduction; Linear discriminant analysis; Multi-labeled problems; Text categorization BibRef

Fu, Y.[Yun], Li, Z.[Zhu], Huang, T.S.[Thomas S.], Katsaggelos, A.K.[Aggelos K.],
Locally adaptive subspace and similarity metric learning for visual data clustering and retrieval,
CVIU(110), No. 3, June 2008, pp. 390-402.
WWW Version. 0711
Locally embedded analysis; Locally embedded clustering; Locally adaptive retrieval; Manifold; Subspace learning; Dimensionality reduction; Similarity matching; Image and video retrieval; Visual clustering BibRef

Guo, Y.[Yi], Gao, J.B.[Jun-Bin], Kwan, P.W.[Paul W.],
Twin Kernel Embedding,
PAMI(30), No. 8, August 2008, pp. 1490-1495.
IEEE DOI Link 0806
BibRef

Rueda, L.G.[Luis G.], Herrera, M.[Myriam],
Linear dimensionality reduction by maximizing the Chernoff distance in the transformed space,
PR(41), No. 10, October 2008, pp. 3138-3152.
WWW Version. 0808
BibRef
Earlier:
A New Approach to Multi-class Linear Dimensionality Reduction,
CIARP06(634-643).
Springer DOI Link 0611
BibRef
And:
A Theoretical Comparison of Two Linear Dimensionality Reduction Techniques,
CIARP06(624-633).
Springer DOI Link 0611
Linear dimensionality reduction; Pattern classification; Discriminant analysis See also On Optimal Pairwise Linear Classifiers for Normal Distributions: The D-Dimensional Case. BibRef

Rueda, L.G.[Luis G.], Herrera, M.[Myriam],
A theoretical comparison of two-class Fisher's and heteroscedastic linear dimensionality reduction schemes,
PRL(29), No. 16, 1 December 2008, pp. 2092-2098.
WWW Version. 0811
Linear dimensionality reduction; Heteroscedastic classifiers; Classification error BibRef

Rueda, L.G.[Luis G.], Oommen, B.J.[B. John], Henriquez, C.[Claudio],
Multi-class pairwise linear dimensionality reduction using heteroscedastic schemes,
PR(43), No. 7, July 2010, pp. 2456-2465.
Elsevier DOI Link
WWW Version. 1003
BibRef
Earlier: A1, A3, A2:
Chernoff-Based Multi-class Pairwise Linear Dimensionality Reduction,
CIARP08(301-308).
Springer DOI Link 0809
Linear dimensionality reduction; Fisher's discriminant analysis; Heteroscedastic discriminant analysis; Chernoff-based dimensionality reduction; Pairwise multi-class classification BibRef

Shen, C.H.[Chun-Hua], Li, H.D.[Hong-Dong], Brooks, M.J.[Michael J.],
Supervised dimensionality reduction via sequential semidefinite programming,
PR(41), No. 12, December 2008, pp. 3644-3652.
WWW Version. 0810
Dimensionality reduction; Semidefinite programming; Linear discriminant analysis BibRef

Shen, C.H.[Chun-Hua], Kim, J.[Junae], Wang, L.[Lei],
A scalable dual approach to semidefinite metric learning,
CVPR11(2601-2608).
IEEE DOI Link 1106
BibRef

Scoleri, T., Chojnacki, W., Brooks, M.J.[Michael J.],
Dimensionality reduction for more stable vision parameter estimation,
IET-CV(2), No. 4, December 2008, pp. 218-227.
WWW Version. 0905
BibRef

Scoleri, T.,
Post-hoc Correction Techniques for Constrained Parameter Estimation in Computer Vision,
DICTA08(412-419).
IEEE DOI Link 0812
BibRef

Lisboa, P.J.G.[Paulo J.G.], Ellis, I.O., Green, A.R., Ambrogi, F., Dias, M.B.,
Cluster-based visualisation with scatter matrices,
PRL(29), No. 13, 1 October 2008, pp. 1814-1823.
WWW Version. 0804
Visualisation; Dimensionality reduction; Breast cancer; Marketing; Conjoint analysis BibRef

Romero, E.[Enrique], Mu, T.T.[Ting-Ting], Lisboa, P.J.G.[Paulo J.G.],
Cohort-based kernel visualisation with scatter matrices,
PR(45), No. 4, April 2012, pp. 1436-1454.
Elsevier DOI Link
WWW Version. 1112
Visualisation; Discriminant analysis; Kernel method BibRef

Nie, F.P.[Fei-Ping], Xiang, S.M.[Shi-Ming], Song, Y.Q.[Yang-Qiu], Zhang, C.S.[Chang-Shui],
Extracting the optimal dimensionality for local tensor discriminant analysis,
PR(42), No. 1, January 2009, pp. 105-114.
WWW Version. 0809
Optimal dimensionality; Local scatter; Tensor discriminant analysis; Alternating optimization BibRef

Nie, F.P.[Fei-Ping], Xiang, S.M.[Shi-Ming], Jia, Y.Q.[Yang-Qing], Zhang, C.S.[Chang-Shui],
Semi-supervised orthogonal discriminant analysis via label propagation,
PR(42), No. 11, November 2009, pp. 2615-2627.
Elsevier DOI Link
WWW Version. 0907
Subspace learning; Discriminant analysis; Dimensionality reduction; Trace ratio; Semi-supervised learning BibRef

Nie, F.P.[Fei-Ping], Xu, D., Li, X., Xiang, S.M.[Shi-Ming],
Semisupervised Dimensionality Reduction and Classification Through Virtual Label Regression,
SMC-B(41), No. 3, June 2011, pp. 675-685.
IEEE DOI Link 1106
BibRef

Hou, C., Nie, F.P.[Fei-Ping], Zhang, C.S.[Chang-Shui], Wu, Y.,
Learning an Orthogonal and Smooth Subspace for Image Classification,
SPLetters(16), No. 4, April 2009, pp. 303-306.
IEEE DOI Link 0903
BibRef

Liu, Y.[Yang], Liu, Y.[Yan], Chan, K.C.C.[Keith C.C.],
Dimensionality reduction for heterogeneous dataset in rushes editing,
PR(42), No. 2, February 2009, pp. 229-242.
WWW Version. 0810
Dimensionality reduction; Rushes editing; Manifold learning; Isometric feature mapping; Multi-layer Isometric feature mapping BibRef

Liu, Y.[Yang], Liu, Y.[Yan], Chan, K.C.C.[Keith C.C.],
Tensor-based locally maximum margin classifier for image and video classification,
CVIU(115), No. 3, March 2011, pp. 300-309.
Elsevier DOI Link
WWW Version. 1103
BibRef
Earlier:
Multilinear Isometric Embedding for visual pattern analysis,
Subspace09(212-218).
IEEE DOI Link 0910
Image and video classification; Local-based method; Maximum margin classifier; Tensor representation BibRef

Xu, D.[Dong], Yan, S.C.[Shui-Cheng], Lin, S.[Stephen], Huang, T.S.[Thomas S.],
Convergent 2-D Subspace Learning With Null Space Analysis,
CirSysVideo(18), No. 12, December 2008, pp. 1753-1759.
IEEE DOI Link 0812
See also Reconstruction and Recognition of Tensor-Based Objects With Concurrent Subspaces Analysis. BibRef

Xu, D.[Dong], Yan, S.C.[Shui-Cheng], Lin, S.[Stephen], Huang, T.S.[Thomas S.], Chang, S.F.[Shih-Fu],
Enhancing Bilinear Subspace Learning by Element Rearrangement,
PAMI(31), No. 10, October 2009, pp. 1913-1920.
IEEE DOI Link 0909
BibRef

Xu, D., Yan, X.,
Semi-Supervised Bilinear Subspace Learning,
IP(18), No. 7, July 2009, pp. 1671-1676.
IEEE DOI Link 0906
BibRef

Yan, S.C.[Shui-Cheng], Xu, D.[Dong], Lin, S.[Stephen], Huang, T.S.[Thomas S.], Chang, S.F.[Shih-Fu],
Element Rearrangement for Tensor-Based Subspace Learning,
CVPR07(1-8).
IEEE DOI Link 0706
BibRef

Pang, Y., Yuan, Y., Li, X.L.,
Effective Feature Extraction in High-Dimensional Space,
SMC-B(38), No. 6, December 2008, pp. 1652-1656.
IEEE DOI Link 0812
BibRef

Pang, Y., Yuan, Y., Li, X.L.,
Iterative Subspace Analysis Based on Feature Line Distance,
IP(18), No. 4, April 2009, pp. 903-907.
IEEE DOI Link 0903
BibRef

Fan, M.Y.[Ming-Yu], Qiao, H.[Hong], Zhang, B.[Bo],
Intrinsic dimension estimation of manifolds by incising balls,
PR(42), No. 5, May 2009, pp. 780-787.
Elsevier DOI Link
WWW Version. 0902
Nonlinear dimensionality reduction; Manifold learning; Intrinsic dimension estimation; Data mining BibRef

Fan, M.Y.[Ming-Yu], Gu, N.[Nannan], Qiao, H.[Hong], Zhang, B.[Bo],
Sparse regularization for semi-supervised classification,
PR(44), No. 8, August 2011, pp. 1777-1784.
Elsevier DOI Link
WWW Version. 1104
Regularization theory; Semi-supervised learning; Regularized least square classification; Dimensionality reduction BibRef

Pan, Y.Z.[Yao-Zhang], Ge, S.Z.S.[Shu-Zhi Sam], Al Mamun, A.[Abdullah],
Weighted locally linear embedding for dimension reduction,
PR(42), No. 5, May 2009, pp. 798-811.
Elsevier DOI Link
WWW Version. 0902
Nonlinear dimensionality reduction; Manifold learning; Feature extraction; Locally linear embedding BibRef

Ge, S.Z.S.[Shu-Zhi Sam], Guan, F.[Feng], Pan, Y.Z.[Yao-Zhang], Loh, A.P.[Ai Poh],
Neighborhood linear embedding for intrinsic structure discovery,
MVA(21), No. 3, April 2010, pp. xx-yy.
Springer DOI Link 1003
Learning to discover neighborhood relationships. BibRef

Yan, S.C.[Shui-Cheng], Wang, H.[Huan], Tu, J., Tang, X.[Xiaoou], Huang, T.S.[Thomas S.],
Mode-kn Factor Analysis for Image Ensembles,
IP(18), No. 3, March 2009, pp. 670-676.
IEEE DOI Link 0903
BibRef

Wang, H.[Huan], Yan, S.C.[Shui-Cheng], Xu, D.[Dong], Tang, X.[Xiaoou], Huang, T.S.[Thomas S.],
Trace Ratio vs. Ratio Trace for Dimensionality Reduction,
CVPR07(1-8).
IEEE DOI Link 0706
BibRef

Renard, N., Bourennane, S.,
Dimensionality Reduction Based on Tensor Modeling for Classification Methods,
GeoRS(47), No. 4, April 2009, pp. 1123-1131.
IEEE DOI Link 0903
BibRef

Felsberg, M.[Michael], Kalkan, S.[Sinan], Kruger, N.[Norbert],
Continuous dimensionality characterization of image structures,
IVC(27), No. 6, 4 May 2009, pp. 628-636.
Elsevier DOI Link
WWW Version. 0904
Intrinsic dimensionality; Feature extraction and classification BibRef

Kim, J.K.[Jong Kyoung], Choi, S.J.[Seung-Jin],
Clustering with r-regular graphs,
PR(42), No. 9, September 2009, pp. 2020-2028.
Elsevier DOI Link
WWW Version. 0905
b-Matching; Cluster utility; Graph-based clustering; Regular graphs BibRef

Lee, S.H.[Seung-Hak], Choi, S.J.[Seung-Jin],
Landmark MDS ensemble,
PR(42), No. 9, September 2009, pp. 2045-2053.
Elsevier DOI Link
WWW Version. 0905
Dimensionality reduction; Embedding; Multidimensional scaling (MDS); Unsupervised learning BibRef

Hou, C.P.[Chen-Ping], Zhang, C.S.[Chang-Shui], Wu, Y.[Yi], Jiao, Y.Y.[Yuan-Yuan],
Stable local dimensionality reduction approaches,
PR(42), No. 9, September 2009, pp. 2054-2066.
Elsevier DOI Link
WWW Version. 0905
Dimensionality reduction; Manifold learning; Locally linear embedding; Laplacian eigenmaps; Local tangent space alignment BibRef

Hou, C.P.[Chen-Ping], Zhang, C.S.[Chang-Shui], Wu, Y.[Yi], Nie, F.P.[Fei-Ping],
Multiple view semi-supervised dimensionality reduction,
PR(43), No. 3, March 2010, pp. 720-730.
Elsevier DOI Link
WWW Version. 1001
Dimensionality reduction; Semi-supervised; Multiple view; Domain knowledge BibRef

Cho, M.K.[Min-Kook], Park, H.Y.[Hye-Young],
A feature analysis for dimension reduction based on a data generation model with class factors and environment factors,
CVIU(113), No. 9, September 2009, pp. 1005-1016.
Elsevier DOI Link
WWW Version. 0907
Pattern classification; Feature analysis; Dimension reduction; PCA (principal component analysis); LDA (linear discriminant analysis); Data generation model; Class factor; Environment factor BibRef

Li, J.[Jun], Hao, P.W.[Peng-Wei],
Finding representative landmarks of data on manifolds,
PR(42), No. 11, November 2009, pp. 2335-2352.
Elsevier DOI Link
WWW Version. 0907
Manifold learning; Data representation; Dimensionality reduction BibRef

Gullo, F.[Francesco], Ponti, G.[Giovanni], Tagarelli, A.[Andrea], Greco, S.[Sergio],
A time series representation model for accurate and fast similarity detection,
PR(42), No. 11, November 2009, pp. 2998-3014.
Elsevier DOI Link
WWW Version. 0907
Time series data; Representation models; Similarity detection; Dimensionality reduction; Clustering; Classification BibRef

Hu, X.Q.[Xiao-Qin], Yang, Z.[Zhixia], Jing, L.[Ling],
An incremental dimensionality reduction method on discriminant information for pattern classification,
PRL(30), No. 15, 1 November 2009, pp. 1416-1423,.
Elsevier DOI Link
WWW Version. 0910
Dimensionality reduction; Pattern classification; Discriminant mapping BibRef

Zhang, T., Huang, K., Li, X., Yang, J., Tao, D.,
Discriminative Orthogonal Neighborhood-Preserving Projections for Classification,
SMC-B(40), No. 1, February 2010, pp. 253-263.
IEEE DOI Link 0911
To overcome outlier problems in linear embedded classification. BibRef

Jia, P.[Peng], Yin, J.S.[Jun-Song], Huang, X.S.[Xin-Sheng], Hu, D.[Dewen],
Incremental Laplacian eigenmaps by preserving adjacent information between data points,
PRL(30), No. 16, 1 December 2009, pp. 1457-1463,.
Elsevier DOI Link
WWW Version. 0911
Laplacian eigenmaps; Incremental learning; Locally linear construction; Nonlinear dimensionality reduction BibRef

Dianat, R., Kasaei, S.,
Dimension Reduction of Optical Remote Sensing Images via Minimum Change Rate Deviation Method,
GeoRS(48), No. 1, January 2010, pp. 198-206.
IEEE DOI Link 1001
BibRef

Hsieh, P.F.[Pi-Fuei], Chou, P.W.[Po-Wen], Chung, H.Y.[Hsueh-Yi],
An MRF-based kernel method for nonlinear feature extraction,
IVC(28), No. 3, March 2010, pp. 502-517.
Elsevier DOI Link
WWW Version. 1001
Feature extraction; Dimensionality reduction; Kernel trick; Classification BibRef

Wang, J.[Jing], Zhang, Z.Y.[Zhen-Yue],
Nonlinear embedding preserving multiple local-linearities,
PR(43), No. 4, April 2010, pp. 1257-1268.
Elsevier DOI Link
WWW Version. 1002
Manifold learning; Dimensionality reduction; Weight vector; Stability of algorithm BibRef

Zhang, Z.Y.[Zhen-Yue], Wang, J.[Jing], Zha, H.Y.[Hong-Yuan],
Adaptive Manifold Learning,
PAMI(34), No. 2, February 2012, pp. 253-265.
IEEE DOI Link 1112
Seek low-dimensional parameterization of high-dimensional data. Assume local can approximate global. BibRef

Liang, Z.Z.[Zhi-Zheng], Li, Y.F.[You-Fu],
A regularization framework for robust dimensionality reduction with applications to image reconstruction and feature extraction,
PR(43), No. 4, April 2010, pp. 1269-1281.
Elsevier DOI Link
WWW Version. 1002
Regularization framework; Nonlinear eigenvalue problem; SCF iteration; Robust; Feature extraction; Image reconstruction BibRef

Chu, D.L.[De-Lin], Thye, G.S.[Goh Siong],
A new and fast implementation for null space based linear discriminant analysis,
PR(43), No. 4, April 2010, pp. 1373-1379.
Elsevier DOI Link
WWW Version. 1002
Dimensionality reduction; Linear discriminant analysis; Null space based linear discriminant analysis; QR factorization; Singular value decomposition BibRef

Czarnowski, I.[Ireneusz],
Prototype selection algorithms for distributed learning,
PR(43), No. 6, June 2010, pp. 2292-2300.
Elsevier DOI Link
WWW Version. 1003
Distributed data mining; Distributed learning; Data reduction; Instance selection BibRef

Liu, R.S.[Ri-Sheng], Lin, Z.C.[Zhou-Chen], Su, Z.X.[Zhi-Xun], Tang, K.W.[Ke-Wei],
Feature extraction by learning Lorentzian metric tensor and its extensions,
PR(43), No. 10, October 2010, pp. 3298-3306.
Elsevier DOI Link
WWW Version. 1007
Feature extraction; Dimensionality reduction; Lorentzian geometry; Metric learning; Discriminant analysis BibRef

Lin, B.B.[Bin-Bin], He, X.F.[Xiao-Fei], Zhou, Y.[Yuan], Liu, L.G.[Li-Gang], Lu, K.[Ke],
Approximately harmonic projection: Theoretical analysis and an algorithm,
PR(43), No. 10, October 2010, pp. 3307-3313.
Elsevier DOI Link
WWW Version. 1007
Manifold learning; Dimensionality reduction; Linear projection; Harmonic function BibRef

Qu, H.N.[Hai-Ni], Li, G.Z.[Guo-Zheng], Xu, W.S.[Wei-Sheng],
An asymmetric classifier based on partial least squares,
PR(43), No. 10, October 2010, pp. 3448-3457.
Elsevier DOI Link
WWW Version. 1007
Partial least squares; Dimension reduction; Classification; Unbalanced data BibRef

Nie, F.P.[Fei-Ping], Xu, D.[Dong], Tsang, I.W.H., Zhang, C.S.[Chang-Shui],
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction,
IP(19), No. 7, July 2010, pp. 1921-1932.
IEEE DOI Link 1007
BibRef

Tasoulis, S.K., Tasoulis, D.K., Plagianakos, V.P.,
Enhancing principal direction divisive clustering,
PR(43), No. 10, October 2010, pp. 3391-3411.
Elsevier DOI Link
WWW Version. 1007
Clustering; Principal component analysis; Kernel density estimation BibRef

Lewandowski, M.[Michal], Makris, D.[Dimitrios], Nebel, J.C.[Jean-Christophe],
Automatic configuration of spectral dimensionality reduction methods,
PRL(31), No. 12, 1 September 2010, pp. 1720-1727.
Elsevier DOI Link
WWW Version. 1008
Dimensionality reduction; Locally Linear Embedding; Isomap; Laplacian Eigenmaps; Mutual information; Radial Basis Function network BibRef

Lee, J.A.[John A.], Verleysen, M.[Michel],
Scale-independent quality criteria for dimensionality reduction,
PRL(31), No. 14, 15 October 2010, pp. 2248-2257.
Elsevier DOI Link
WWW Version. 1003
Dimensionality reduction; Embedding; Manifold learning; Quality assessment BibRef

Wang, J.Z.[Jian-Zhong], Zhang, B.[Baoxue], Qi, M.[Miao], Kong, J.[Jun],
Linear discriminant projection embedding based on patches alignment,
IVC(28), No. 12, December 2010, pp. 1624-1636.
Elsevier DOI Link
WWW Version. 1003
Dimensionality reduction; Manifold learning; Patches alignment; Face recognition; Maximum margin criterion BibRef

Kaban, A.[Ata],
On the distance concentration awareness of certain data reduction techniques,
PR(44), No. 2, February 2011, pp. 265-277.
Elsevier DOI Link
WWW Version. 1011
Distance concentration; Dimensionality reduction; Feature selection; Projection pursuit; Sure independence screening BibRef

Yu, Y.L.[Yao-Liang], Jiang, J.Y.[Jia-Yan], Zhang, L.M.[Li-Ming],
Distance metric learning by minimal distance maximization,
PR(44), No. 3, March 2011, pp. 639-649.
Elsevier DOI Link
WWW Version. 1011
Linear dimensionality reduction (LDR); Metric learning; Convex optimization; Minimal distance maximization BibRef

Ailon, N.[Nir], Chazelle, B.[Bernard],
Faster Dimension Reduction,
CACM(53), No. 2, February 2010, pp. 97-104.
WWW Version. 1101
Data represented geometrically in high-dimensional vector spaces can be found in many applications. Images and videos, are often represented by assigning a dimension for every pixel (and time). BibRef

Zhang, P.[Peng], Qiao, H.[Hong], Zhang, B.[Bo],
An improved local tangent space alignment method for manifold learning,
PRL(32), No. 2, 15 January 2011, pp. 181-189.
Elsevier DOI Link
WWW Version. 1101
Nonlinear dimensionality reduction; Manifold learning; Data mining BibRef

Salamo, M.[Maria], Lopez-Sanchez, M.[Maite],
Rough set based approaches to feature selection for Case-Based Reasoning classifiers,
PRL(32), No. 2, 15 January 2011, pp. 280-292.
Elsevier DOI Link
WWW Version. 1101
Feature selection; Dimensionality reduction; Classification techniques; Case-Based Reasoning; Rough Set Theory BibRef

Villegas, M.[Mauricio], Paredes, R.[Roberto],
Dimensionality reduction by minimizing nearest-neighbor classification error,
PRL(32), No. 4, 1 March 2011, pp. 633-639.
Elsevier DOI Link
WWW Version. 1102
Dimensionality reduction; Pattern recognition; Nearest-neighbor classifier BibRef

Shang, F.[Fanhua], Jiao, L.C., Shi, J.[Jiarong], Chai, J.[Jing],
Robust Positive semidefinite L-Isomap Ensemble,
PRL(32), No. 4, 1 March 2011, pp. 640-649.
Elsevier DOI Link
WWW Version. 1102
Dimensionality reduction; Manifold learning; Nystrom approximation; Isomap; Ensemble learning; High dimensional affine transformation BibRef

Chen, W.Y.[Wen-Yen], Song, Y.Q.[Yang-Qiu], Bai, H.J.[Hong-Jie], Lin, C.J.[Chih-Jen], Chang, E.Y.[Edward Y.],
Parallel Spectral Clustering in Distributed Systems,
PAMI(33), No. 3, March 2011, pp. 568-586.
IEEE DOI Link 1102
(from Yahoo, Microsoft and Google) Over a large set of documents and images. BibRef

Kim, M.Y.[Min-Young], Pavlovic, V.[Vladimir],
Central Subspace Dimensionality Reduction Using Covariance Operators,
PAMI(33), No. 4, April 2011, pp. 657-670.
IEEE DOI Link 1103
BibRef
Earlier:
Dimensionality reduction using covariance operator inverse regression,
CVPR08(1-8).
IEEE DOI Link 0806
BibRef

Gao, X., Wang, X., Tao, D., Li, X.,
Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction,
SMC-B(41), No. 2, April 2011, pp. 425-434.
IEEE DOI Link 1103
BibRef

Wu, J., Zhang, X.L.,
Maximum Margin Clustering Based Statistical VAD With Multiple Observation Compound Feature,
SPLetters(18), No. 5, May 2011, pp. 283-286.
IEEE DOI Link 1103
BibRef

Park, H.[Heum], Kwon, H.C.[Hyuk-Chul],
Improved Gini-Index Algorithm to Correct Feature-Selection Bias in Text Classification,
IEICE(E94-D), No. 4, April 2011, pp. 855-865.
WWW Version. 1104
Gini-index used for splitting measure in decision tree. BibRef

Li, H.S.[Hou-Sen], Jiang, H.[Hao], Barrio, R.[Roberto], Liao, X.K.[Xiang-Ke], Cheng, L.Z.[Li-Zhi], Su, F.[Fang],
Incremental manifold learning by spectral embedding methods,
PRL(32), No. 10, 15 July 2011, pp. 1447-1455.
Elsevier DOI Link
WWW Version. 1106
Manifold learning; Incremental learning; Dimensionality reduction; Spectral embedding methods; Hessian eigenmaps BibRef

Chang, C.I.[Chein-I], Safavi, H.[Haleh],
Progressive dimensionality reduction by transform for hyperspectral imagery,
PR(44), No. 10-11, October-November 2011, pp. 2760-2773.
Elsevier DOI Link
WWW Version. 1101
Backward progressive dimensionality reduction by PI-PP (BPDR-PIPP); Dimensionality reduction by transform (DRT); Forward progressive dimensionality reduction by PI-PP (FPDR-PIPP); Progressive dimensionality reduction by projection index-based projection pursuit (PDR-PIPP); Progressive dimensionality reduction by transform (PDRT); Projection index-based projection pursuit (PIPP) BibRef

Wang, R.P.[Rui-Ping], Shan, S.G.[Shi-Guang], Chen, X.L.[Xi-Lin], Chen, J.[Jie], Gao, W.[Wen],
Maximal Linear Embedding for Dimensionality Reduction,
PAMI(33), No. 9, September 2011, pp. 1776-1792.
IEEE DOI Link 1109
BibRef

Wong, W.K., Zhao, H.T.,
Supervised optimal locality preserving projection,
PR(45), No. 1, January 2012, pp. 186-197.
Elsevier DOI Link
WWW Version. 1109
Classification; Feature extraction; Dimensionality reduction; Manifold learning BibRef

Lai, Z.H.[Zhi-Hui], Jin, Z.[Zhong], Wong, W.K.,
Tangent space discriminant analysis for feature extraction,
ICIP10(3793-3796).
IEEE DOI Link 1009
BibRef

van de Ville, D., Kocher, M.,
Nonlocal Means With Dimensionality Reduction and SURE-Based Parameter Selection,
IP(20), No. 9, September 2011, pp. 2683-2690.
IEEE DOI Link 1109
BibRef

Bouveyron, C.[Charles], Celeux, G.[Gilles], Girard, S.C.[Stéphane C.],
Intrinsic dimension estimation by maximum likelihood in isotropic probabilistic PCA,
PRL(32), No. 14, 15 October 2011, pp. 1706-1713.
Elsevier DOI Link
WWW Version. 1110
Probabilistic PCA; Isotropic model; Dimension reduction; Intrinsic dimension; Maximum likelihood; Asymptotic consistency BibRef

Ge, S.S.[Shuzhi Sam], He, H.S.[Hong-Sheng], Shen, C.Y.[Cheng-Yao],
Geometrically local embedding in manifolds for dimension reduction,
PR(45), No. 4, April 2012, pp. 1455-1470.
Elsevier DOI Link
WWW Version. 1112
Geometry distance; Dimension reduction; Linear manifolds; GLE BibRef

Lázaro-Gredilla, M.[Miguel], Van Vaerenbergh, S.[Steven], Lawrence, N.D.[Neil D.],
Overlapping Mixtures of Gaussian Processes for the data association problem,
PR(45), No. 4, April 2012, pp. 1386-1395.
Elsevier DOI Link
WWW Version. 1112
Gaussian Processes; Marginalized variational inference; Bayesian models BibRef

Urtasun, R.[Raquel], Quattoni, A.[Ariadna], Lawrence, N.D.[Neil D.], Darrell, T.J.[Trevor J.],
Transferring Nonlinear Representations using Gaussian Processes with a Shared Latent Space,
CSAIL-2008-020, April 2008.
WWW Version. BibRef 0804

Geiger, A.[Andreas], Urtasun, R.[Raquel], Darrell, T.J.[Trevor J.], Stiefelhagen, R.[Rainer],
Rank Priors for Continuous Non-Linear Dimensionality Reduction,
CSAIL-2008-056, September 2008.
WWW Version. BibRef 0809
And: A1, A2, A3, Only: CVPR09(880-887).
IEEE DOI Link 0906
BibRef

Chen, X.H.[Xiao-Hong], Chen, S.C.[Song-Can], Xue, H.[Hui], Zhou, X.D.[Xu-Dong],
A unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data,
PR(45), No. 5, May 2012, pp. 2005-2018.
Elsevier DOI Link
WWW Version. 1201
Multi-view data; Correlation analysis; Semi-supervised learning; Semi-paired learning; Dimensionality reduction BibRef

Tu, S.T.[Shang Tan], Chen, J.Y.[Jia Yu], Yang, W.[Wen], Sun, H.[Hong],
Laplacian Eigenmaps-Based Polarimetric Dimensionality Reduction for SAR Image Classification,
GeoRS(50), No. 1, January 2012, pp. 170-179.
IEEE DOI Link 1201
BibRef

Faivishevsky, L.[Lev], Goldberger, J.[Jacob],
An unsupervised data projection that preserves the cluster structure,
PRL(33), No. 3, 1 February 2012, pp. 256-262.
Elsevier DOI Link
WWW Version. 1201
Unsupervised dimensionality reduction; Mutual information; Clustering BibRef

Hong, Y.[Yi], Li, Q.N.[Quan-Nan], Jiang, J.Y.[Jia-Yan], Tu, Z.W.[Zhuo-Wen],
Learning a mixture of sparse distance metrics for classification and dimensionality reduction,
ICCV11(906-913).
IEEE DOI Link 1201
neighborhood components analysis. Mixture of sparse metrics BibRef

Rozza, A.[Alessandro], Lombardi, G.[Gabriele], Rosa, M.[Marco], Casiraghi, E.[Elena], Campadelli, P.[Paola],
IDEA: Intrinsic Dimension Estimation Algorithm,
CIAP11(I: 433-442).
Springer DOI Link 1109
Dimensionality reduction for high dimensional data BibRef