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Volumn , Issue , 2011, Pages 1324-1329

Feature selection via joint embedding learning and sparse regression

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE SELECTION METHODS; GRAPH LAPLACIAN; LINEAR APPROXIMATIONS; RESEARCH INTERESTS; SPARSE MATRICES; SPARSE REGRESSION; TRADITIONAL LEARNING; UNSUPERVISED FEATURE SELECTION;

EID: 84866678530     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5591/978-1-57735-516-8/IJCAI11-224     Document Type: Conference Paper
Times cited : (185)

References (14)
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    • Feature selection for clustering - A filter solution
    • Manoranjan Dash, Kiseok Choi, Peter Scheuermann, and Huan Liu. Feature selection for clustering - a filter solution. ICDM '02, pages 115-122, 2002.
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    • Dash, M.1    Choi, K.2    Scheuermann, P.3    Liu, H.4
  • 6
    • 84864039505 scopus 로고    scopus 로고
    • Laplacian score for feature selection
    • Xiaofei He, Deng Cai, and Partha Niyogi. Laplacian score for feature selection. In NIPS, 2005.
    • (2005) NIPS
    • He, X.1    Cai, D.2    Niyogi, P.3
  • 8
    • 57749182885 scopus 로고    scopus 로고
    • Trace ratio criterion for feature selection
    • Feiping Nie, Shiming Xiang, Yangqing Jia, Changshui Zhang, and Shuicheng Yan. Trace ratio criterion for feature selection. In AAAI, 2008.
    • (2008) AAAI
    • Nie, F.1    Xiang, S.2    Jia, Y.3    Zhang, C.4    Yan, S.5
  • 10
    • 77953705810 scopus 로고    scopus 로고
    • Flexible manifold embedding: A framework for semi-supervised and unsupervised dimension reduction
    • Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, and Changshui Zhang. Flexible manifold embedding: A framework for semi-supervised and unsupervised dimension reduction. IEEE TIP, 19(7):1921-1932, 2010.
    • (2010) IEEE TIP , vol.19 , Issue.7 , pp. 1921-1932
    • Nie, F.1    Xu, D.2    Tsang, I.W.-H.3    Zhang, C.4
  • 11
    • 84899029465 scopus 로고    scopus 로고
    • Feature selection in clustering problems
    • Volker Roth and Tilman Lange. Feature selection in clustering problems. In NIPS 16, 2004.
    • (2004) NIPS 16
    • Roth, V.1    Lange, T.2
  • 12
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Sam T. Roweis and Lawrence K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290:2323-2326, 2000.
    • (2000) Science , vol.290 , pp. 2323-2326
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  • 13
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    • Spectral feature selection for supervised and unsupervised learning
    • Zheng Zhao and Huan Liu. Spectral feature selection for supervised and unsupervised learning. In ICML, pages 1151-1157, 2007.
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    • Efficient spectral feature selection with minimum redundancy
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    • Zhao, Z.1    Wang, L.2    Liu, H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.