Keith Price Bibliography Number of Features, Dimensionality, Dimensionality Reduction

14.1.3.2 Number of Features, Dimensionality, Dimensionality Reduction

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

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. IEEE Top Reference.
WWW Version. 0401
BibRef

Buturovic, L.J.,
Toward Bayes-optimal linear dimension reduction,
PAMI(16), No. 4, April 1994, pp. 420-424.
IEEE Abstract. IEEE Top Reference.
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. IEEE Top Reference.
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. IEEE Top Reference.
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

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. IEEE Top Reference.
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. IEEE Top Reference.
WWW Version. 0108
BibRef
Earlier: A2, A1, A3:
Multi-class Linear Feature Extraction by Nonlinear PCA,
ICPR00(Vol II: 398-401).
IEEE DOI Link
HTML Version. 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. IEEE Top Reference. 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. IEEE Top Reference.
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. IEEE Top Reference. 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. IEEE Top Reference. 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. IEEE Top Reference. 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

Martínez Sotoca, J.[José], Pla, F., Salvador Sánchez, J.,
Band Selection in Multispectral Images by Minimization of Dependent Information,
SMC-C(37), No. 2, March 2007, pp. 258-267.
IEEE DOI Link 0703
BibRef

Martínez Sotoca, J.[José], Pla, F.[Filiberto],
Hyperspectral Data Selection from Mutual Information Between Image Bands,
SSPR06(853-861).
Springer DOI Link 0608
BibRef

Martínez Sotoca, J.[José], Salvador Sánchez, J., Pla, F.,
Attribute relevance in multiclass data sets using the naive bayes rule,
ICPR04(III: 426-429).
IEEE DOI Link 0409
BibRef

Martínez Sotoca, J.[José], Pla, F., Klaren, A.C.,
Unsupervised band selection for multispectral images using information theory,
ICPR04(III: 510-513).
IEEE DOI Link 0409
BibRef

Martínez-Usó, A.[Adolfo], Pla, F.[Filiberto], Martínez Sotoca, J.[José], García-Sevilla, P.[Pedro],
Clustering-Based Hyperspectral Band Selection Using Information Measures,
GeoRS(45), No. 12, December 2007, pp. 4158-4171.
IEEE DOI Link 0711
BibRef
Earlier: A1, A2, A4, A3:
Automatic Band Selection in Multispectral Images Using Mutual Information-Based Clustering,
CIARP06(644-654).
Springer DOI Link 0611
BibRef
And: A1, A2, A3, A4:
Clustering-based multispectral band selection using mutual information,
ICPR06(II: 760-763).
WWW Version. 0609
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.], Henríquez, C.[Claudio], Oommen, B.J.[B. John],
Chernoff-Based Multi-class Pairwise Linear Dimensionality Reduction,
CIARP08(301-308).
Springer DOI Link 0809
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

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

Lisboa, P.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

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

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

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.,
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.,
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

Pan, Y.Z.[Yao-Zhang], Ge, S.S.[Shuzhi 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

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
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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
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Renard, N., Bourennane, S.,
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GeoRS(47), No. 4, April 2009, pp. 1123-1131.
IEEE DOI Link 0903
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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.
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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,
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WWW Version. 0905
Dimensionality reduction; Manifold learning; Locally linear embedding; Laplacian eigenmaps; Local tangent space alignment BibRef

Mojaradi, B., Abrishami-Moghaddam, H., Valadan Zoej, M. J., Duin, R.P.W.,
Dimensionality Reduction of Hyperspectral Data via Spectral Feature Extraction,
GeoRS(47), No. 7, July 2009, pp. 2091-2105.
IEEE DOI Link 0906
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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,.
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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,
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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],
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WWW Version. 0911
Laplacian eigenmaps; Incremental learning; Locally linear construction; Nonlinear dimensionality reduction BibRef

Bauckhage, C.[Christian], Thurau, C.[Christian],
Making Archetypal Analysis Practical,
DAGM09(272-281).
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Represent as combination of extremal points. BibRef

Sáenz, C.[Carlos], Hernández, B.[Begoña], Alberdi, C.[Coro], Alfonso, S.[Santiago], Diñeiro, J.M.[José Manuel],
The Number of Linearly Independent Vectors in Spectral Databases,
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