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Volumn , Issue , 2009, Pages 132-136

Global sparse representation projections for feature extraction and classification

Author keywords

Dimensionality reduction; Feature extraction; Manifold learning; Sparse representation

Indexed keywords

DIMENSIONALITY REDUCTION; FEATURE EXTRACTION AND CLASSIFICATION; LINEAR DIMENSIONALITY REDUCTION; LOCAL STRUCTURE; MANIFOLD LEARNING; OBJECTIVE FUNCTIONS; RECOGNITION RATES; SPARSE REPRESENTATION; SUPERVISED LEARNING METHODS;

EID: 74549196418     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CCPR.2009.5344136     Document Type: Conference Paper
Times cited : (12)

References (22)
  • 6
    • 0347243182 scopus 로고    scopus 로고
    • Nonlinear Component Analysis as a Kernel Eigenvalue Problem
    • B. Schölkopf, A. Smola, and K.R. Muller, "Nonlinear Component Analysis as a Kernel Eigenvalue Problem," Neural Computation, vol. 10, no. 5, pp. 1299-1319, 1998.
    • (1998) Neural Computation , vol.10 , Issue.5 , pp. 1299-1319
    • Schölkopf, B.1    Smola, A.2    Muller, K.R.3
  • 7
    • 14544297033 scopus 로고    scopus 로고
    • KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition
    • Feb
    • J. Yang, A.F. Frangi, D. Zhang, J.-y. Yang, and J. Zhong, "KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 230-244, Feb. 2005.
    • (2005) IEEE Trans. Pattern Analysis and Machine Intelligence , vol.27 , Issue.2 , pp. 230-244
    • Yang, J.1    Frangi, A.F.2    Zhang, D.3    Yang, J.-Y.4    Zhong, J.5
  • 8
    • 0034704229 scopus 로고    scopus 로고
    • A Global Geometric Framework for Nonlinear Dimensionality Reduction
    • J.B. Tenenbaum, V. deSilva, and J.C. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction," Science, vol. 290, pp. 2319-2323, 2000.
    • (2000) Science , vol.290 , pp. 2319-2323
    • Tenenbaum, J.B.1    deSilva, V.2    Langford, J.C.3
  • 9
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear Dimensionality Reduction by Locally Linear Embedding
    • S.T. Roweis and L.K. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding," Science, vol. 290, pp. 2323-2326,2000.
    • (2000) Science , vol.290 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 10
    • 14544307975 scopus 로고    scopus 로고
    • Principal manifolds and nonlinear dimensionality reduction via tangent space alignment
    • Z. Zhang, H. Zha, Principal manifolds and nonlinear dimensionality reduction via tangent space alignment, SIAM J. Sci. Comput. 26 (1) (2004) 313-338.
    • (2004) SIAM J. Sci. Comput , vol.26 , Issue.1 , pp. 313-338
    • Zhang, Z.1    Zha, H.2
  • 11
    • 0043278893 scopus 로고    scopus 로고
    • Laplacian eigenmaps and spectral techniques for embedding and clustering
    • Vancouver, Canada, December
    • M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, in: Proceedings of Advances in Neural Information Processing System, vol. 14, Vancouver, Canada, December 2001.
    • (2001) Proceedings of Advances in Neural Information Processing System , vol.14
    • Belkin, M.1    Niyogi, P.2
  • 13
    • 8644228268 scopus 로고    scopus 로고
    • Locality Preserving Projections
    • Neural Information Processing Systems
    • th Conf. Neural Information Processing Systems, 2003.
    • (2003) th Conf
    • He, X.1    Niyogi, P.2
  • 16
    • 39449104854 scopus 로고    scopus 로고
    • Classification and Feature Extraction by Simplexization
    • Information Forensics and Security
    • Y. Fu, S. Yan, and T.S. Huang, "Classification and Feature Extraction by Simplexization," IEEE Trans. Information Forensics and Security, vol. 3, no. 1, pp. 91-100, 2008.
    • (2008) IEEE Trans , vol.3 , Issue.1 , pp. 91-100
    • Fu, Y.1    Yan, S.2    Huang, T.S.3
  • 17
    • 48149086066 scopus 로고    scopus 로고
    • Feature extraction using constrained maximum variance mapping
    • L. Bo, H. De-Shuang, W. Chao, and L. Kun-Hong, "Feature extraction using constrained maximum variance mapping", Pattern Recognition, vol.41, no.11, pp.3287-3294.
    • Pattern Recognition , vol.41 , Issue.11 , pp. 3287-3294
    • Bo, L.1    De-Shuang, H.2    Chao, W.3    Kun-Hong, L.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.