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Volumn 1, Issue , 2015, Pages 470-476

Embedded unsupervised feature selection

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FEATURE EXTRACTION; LEARNING ALGORITHMS; OPTIMIZATION;

EID: 84959482669     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (198)

References (31)
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    • (2011) Foundations and Trends® in Machine Learning , vol.3 , Issue.1 , pp. 1-122
    • Boyd, S.1    Parikh, N.2    Chu, E.3    Peleato, B.4    Eckstein, J.5
  • 8
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    • Fast alternating direction optimization methods
    • Tom Goldstein, Brendan O'Donoghue, and Simon Setzer. Fast alternating direction optimization methods. CAM report, pages 12-35, 2012.
    • (2012) CAM Report , pp. 12-35
    • Goldstein, T.1    O'Donoghue, B.2    Setzer, S.3
  • 14
    • 85099325734 scopus 로고
    • Irrelevant features and the subset selection problem
    • George H John, Ron Kohavi, Karl Pfleger, et al. Irrelevant features and the subset selection problem. In ICML, volume 94, pages 121-129, 1994.
    • (1994) ICML , vol.94 , pp. 121-129
    • John, G.H.1    Kohavi, R.2    Pfleger, K.3
  • 19
    • 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, volume 2, pages 671-676, 2008.
    • (2008) AAAI , vol.2 , pp. 671-676
    • Nie, F.1    Xiang, S.2    Jia, Y.3    Zhang, C.4    Yan, S.5
  • 22
    • 0000988974 scopus 로고
    • A generalized solution of the orthogonal procrustes problem
    • Peter H Schönemann. A generalized solution of the orthogonal procrustes problem. Psychometrika, 31(1): 1-10, 1966.
    • (1966) Psychometrika , vol.31 , Issue.1 , pp. 1-10
    • Schönemann, P.H.1
  • 27
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    • A tutorial on spectral clustering
    • Ulrike Von Luxburg. A tutorial on spectral clustering. Statis tics and computing, 17(4):395-416, 2007.
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    • Von Luxburg, U.1
  • 28
    • 27844550205 scopus 로고    scopus 로고
    • Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weight-based approach
    • Lior Wolf and Amnon Shashua. Feature selection for unsupervised and supervised inference: The emergence of sparsity in a weight-based approach. The Journal of Machine Learning Research, 6:1855-1887, 2005.
    • (2005) The Journal of Machine Learning Research , vol.6 , pp. 1855-1887
    • Wolf, L.1    Shashua, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.