메뉴 건너뛰기




Volumn 33, Issue 10, 2011, Pages 2013-2025

A variance minimization criterion to feature selection using laplacian regularization

Author keywords

clustering.; dimensionality reduction; Feature selection; manifold; regression; regularization

Indexed keywords

CLUSTERING.; DIMENSIONALITY REDUCTION; MANIFOLD; REGRESSION; REGULARIZATION;

EID: 80051962608     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2011.44     Document Type: Article
Times cited : (77)

References (35)
  • 2
  • 3
    • 0043278893 scopus 로고    scopus 로고
    • Laplacian eigenmaps and spectral techniques for embedding and clustering
    • MIT Press
    • M. Belkin and P. Niyogi, "Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering," Advances in Neural Information Processing Systems 14, pp. 585-591, MIT Press, 2001.
    • (2001) Advances in Neural Information Processing Systems , vol.14 , pp. 585-591
    • Belkin, M.1    Niyogi, P.2
  • 4
    • 33750729556 scopus 로고    scopus 로고
    • Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
    • M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold Regulariza-tion: A Geometric Framework for Learning from Examples," J. Machine Learning Research, vol. 7, pp. 2399-2434, 2006. (Pubitemid 44708005)
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 2399-2434
    • Belkin, M.1    Niyogi, P.2    Sindhwani, V.3
  • 6
    • 67650370037 scopus 로고    scopus 로고
    • A hybrid feature extraction selection approach for high-dimensional non-gaussian data clustering
    • Aug.
    • S. Boutemedjet, N. Bouguila, and D. Ziou, "A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 8, pp. 1429-1443, Aug. 2009.
    • (2009) IEEE Trans. Pattern Analysis and Machine Intelligence , vol.31 , Issue.8 , pp. 1429-1443
    • Boutemedjet, S.1    Bouguila, N.2    Ziou, D.3
  • 8
    • 84951832269 scopus 로고    scopus 로고
    • of Regional Conf. Series in Math. AMS
    • F.R.K. Chung, Spectral Graph Theory, vol. 92 of Regional Conf. Series in Math. AMS, 1997.
    • (1997) Spectral Graph Theory , vol.92
    • Chung, F.R.K.1
  • 11
    • 0002878444 scopus 로고    scopus 로고
    • Feature subset selection and order identification for unsupervised leanring
    • J.G. Dy and C.E. Brodley, "Feature Subset Selection and Order Identification for Unsupervised Leanring," Proc. 17th Int'l Conf. Machine Learning, 2000.
    • (2000) Proc. 17th Int'l Conf. Machine Learning
    • Dy, J.G.1    Brodley, C.E.2
  • 14
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machines
    • DOI 10.1023/A:1012487302797
    • I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene Selection for Cancer Classification Using Support Vector Machines," Machine Learning, vol. 46, pp. 389-422, 2002. (Pubitemid 34129977)
    • (2002) Machine Learning , vol.46 , Issue.1-3 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3    Vapnik, V.4
  • 20
    • 72949091678 scopus 로고    scopus 로고
    • Deterministic column-based matrix decomposition
    • Jan.
    • X. Li and Y. Pang, "Deterministic Column-Based Matrix Decomposition," IEEE Trans. Knowledge and Data Eng., vol. 22, no. 1, pp. 145-149, Jan. 2010.
    • (2010) IEEE Trans. Knowledge and Data Eng. , vol.22 , Issue.1 , pp. 145-149
    • Li, X.1    Pang, Y.2
  • 23
    • 0141990695 scopus 로고    scopus 로고
    • Theoretical and empirical analysis of relief and relieff
    • M. Robnik-Sikonja and I. Kononenko, "Theoretical and Empirical Analysis of Relief and Relieff," Machine Learning, vol. 53, nos. 1/2, pp. 23-69, 2003.
    • (2003) Machine Learning , vol.53 , Issue.1-2 , pp. 23-69
    • Robnik-Sikonja, M.1    Kononenko, I.2
  • 24
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • DOI 10.1126/science.290.5500.2323
    • S. Roweis and L. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding," Science, vol. 290, no. 5500, pp. 2323-2326, 2000. (Pubitemid 32041578)
    • (2000) Science , vol.290 , Issue.5500 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 25
    • 0033296299 scopus 로고    scopus 로고
    • Using sedumi 1.02, a matlab toolbox for optimization over symmetric cones
    • J. Sturm, "Using Sedumi 1.02, a Matlab Toolbox for Optimization over Symmetric Cones," Optimization Methods and Software, vol. 11, nos. 1-4, pp. 625-653, 1999.
    • (1999) Optimization Methods and Software , vol.11 , Issue.1-4 , pp. 625-653
    • Sturm, J.1
  • 28
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • DOI 10.1126/science.290.5500.2319
    • J. Tenenbaum, V. de Silva, and J. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction," Science, vol. 290, no. 5500, pp. 2319-2323, 2000. (Pubitemid 32041577)
    • (2000) Science , vol.290 , Issue.5500 , pp. 2319-2323
    • Tenenbaum, J.B.1    De Silva, V.2    Langford, J.C.3
  • 29
    • 0033295027 scopus 로고    scopus 로고
    • Sdpt3-a matlab software package for semidefinite programming
    • K.C. Toh, M.J. Todd, and R.H. Tütüncü, "Sdpt3-A Matlab Software Package for Semidefinite Programming," Optimization Methods and Software, vol. 11, nos. 1-4, pp. 545-581, 1999.
    • (1999) Optimization Methods and Software , vol.11 , Issue.1-4 , pp. 545-581
    • Toh, K.C.1    Todd, M.J.2    Tütüncü, R.H.3
  • 30
    • 0032397913 scopus 로고    scopus 로고
    • Determinant maximization with linear matrix inequality constraints
    • L. Vandenberghe, S. Boyd, and S.-P. Wu, "Determinant Maximization with Linear Matrix Inequality Constraints," SIAM J. Matrix Analysis and Applications, vol. 19, no. 2, pp. 499-533, 1998.
    • (1998) SIAM J. Matrix Analysis and Applications , vol.19 , Issue.2 , pp. 499-533
    • Vandenberghe, L.1    Boyd, S.2    Wu, S.-P.3
  • 31
    • 70350619462 scopus 로고    scopus 로고
    • Information loss of the mahalanobis distance in high dimensions: Application to feature selection
    • Dec.
    • D. Ververidis and C. Kotropoulos, "Information Loss of the Mahalanobis Distance in High Dimensions: Application to Feature Selection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 12, pp. 2275-2281, Dec. 2009.
    • (2009) IEEE Trans. Pattern Analysis and Machine Intelligence , vol.31 , Issue.12 , pp. 2275-2281
    • Ververidis, D.1    Kotropoulos, C.2
  • 32
    • 27844550205 scopus 로고    scopus 로고
    • Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weight-based approach
    • L. Wolf and A. Shashua, "Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach," J. Machine Learning Research, vol. 6, pp. 1855-1887, 2005.
    • (2005) J. Machine Learning Research , vol.6 , pp. 1855-1887
    • Wolf, L.1    Shashua, A.2
  • 34
    • 44649111202 scopus 로고    scopus 로고
    • Locality sensitive semi-supervised feature selection
    • J. Zhao, K. Lu, and X. He, "Locality Sensitive Semi-Supervised Feature Selection," Neurocomputing, vol. 71, nos. 10-12, pp. 1842-1849, 2008.
    • (2008) Neurocomputing , vol.71 , Issue.10-12 , pp. 1842-1849
    • Zhao, J.1    Lu, K.2    He, X.3
  • 35
    • 34547981441 scopus 로고    scopus 로고
    • Spectral feature selection for supervised and unsupervised learning
    • Z. Zhao and H. Liu, "Spectral Feature Selection for Supervised and Unsupervised Learning," Proc. 24th Int'l Conf. Machine Learning, 2007.
    • (2007) Proc. 24th Int'l Conf. Machine Learning
    • Zhao, Z.1    Liu, H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.