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Volumn , Issue , 2009, Pages 1505-1512

Regularized co-clustering with dual supervision

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; ARTIFICIAL INTELLIGENCE; GRAPH THEORY; LINEAR TRANSFORMATIONS; MATRIX ALGEBRA; METADATA;

EID: 84863343714     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (24)

References (15)
  • 2
    • 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 regularization: A geometric framework for learning from labeled and unlabeled examples. JMLR, 7:2399-2434, 2006. (Pubitemid 44708005)
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 2399-2434
    • Belkin, M.1    Niyogi, P.2    Sindhwani, V.3
  • 5
    • 0035789644 scopus 로고    scopus 로고
    • Co-clustering documents and words using bipartite spectral graph partitioning
    • I. Dhillon. Co-clustering documents and words using bipartite spectral graph partitioning. In KDD, 2001.
    • (2001) KDD
    • Dhillon, I.1
  • 6
    • 33749255098 scopus 로고    scopus 로고
    • On the equivalence of nonnegative matrix factorization and spectral clustering
    • C. Ding, X. He, and H.D. Simon. On the equivalence of nonnegative matrix factorization and spectral clustering. In SDM, 2005.
    • (2005) SDM
    • Ding, C.1    He, X.2    Simon, H.D.3
  • 7
    • 33749575326 scopus 로고    scopus 로고
    • Orthogonal nonnegative matrix tri-factorizations for clustering
    • C. Ding, T. Li, W. Peng, and H. Park. Orthogonal nonnegative matrix tri-factorizations for clustering. In KDD, 2006.
    • (2006) KDD
    • Ding, C.1    Li, T.2    Peng, W.3    Park, H.4
  • 8
    • 57349122015 scopus 로고    scopus 로고
    • Learning from labeled features using generalized expectation criteria
    • G. Druck, G. Mann, and A. McCallum. Learning from labeled features using generalized expectation criteria. In SIGIR, 2008.
    • (2008) SIGIR
    • Druck, G.1    Mann, G.2    McCallum, A.3
  • 11
    • 33645620066 scopus 로고    scopus 로고
    • Semi-supervised learning from unbalanced labeled data an improvement
    • T.M. Huang and V. Kecman. Semi-supervised learning from unbalanced labeled data an improvement. Lecture Notes in Computer Science, 3215:765-771, 2004.
    • (2004) Lecture Notes in Computer Science , vol.3215 , pp. 765-771
    • Huang, T.M.1    Kecman, V.2
  • 12
    • 37149010281 scopus 로고    scopus 로고
    • Initializations for the non-negative matrix factorization
    • A. Langville, C. Meyer, and R. Albright. Initializations for the non-negative matrix factorization. In KDD, 2006.
    • (2006) KDD
    • Langville, A.1    Meyer, C.2    Albright, R.3
  • 13
    • 49849097634 scopus 로고    scopus 로고
    • The relationships among various nonnegative matrix factorization methods for clustering
    • T. Li and C. Ding. The relationships among various nonnegative matrix factorization methods for clustering. In ICDM, 2006.
    • (2006) ICDM
    • Li, T.1    Ding, C.2
  • 14
    • 67049165491 scopus 로고    scopus 로고
    • Document- word co-regularization for semi-supervised sentiment analysis
    • V. Sindhwani and P.Melville. Document-word co-regularization for semi-supervised sentiment analysis. In ICDM, 2008.
    • (2008) ICDM
    • Sindhwani, V.1    Melville, P.2
  • 15
    • 0002096830 scopus 로고    scopus 로고
    • Document clustering using word clusters via the information bottleneck method
    • N. Slonim and N. Tishby. Document clustering using word clusters via the information bottleneck method. In SIGIR, 2000.
    • (2000) SIGIR
    • Slonim, N.1    Tishby, N.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.