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Volumn 2008, Issue , 2008, Pages

Probabilistic latent variable models as nonnegative factorizations

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EID: 47649133016     PISSN: 16875265     EISSN: 16875273     Source Type: Journal    
DOI: 10.1155/2008/947438     Document Type: Article
Times cited : (103)

References (34)
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    • A nonnegative pca algorithm for independent component analysis
    • Plumbley M. D., Oja E., A nonnegative pca algorithm for independent component analysis IEEE Transactions on Neural Networks 2004 15 1 66 76
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    • Plumbley, M.D.1    Oja, E.2
  • 3
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • Lee D. D., Seung H. S., Learning the parts of objects by non-negative matrix factorization Nature 1999 401 6755 788 791
    • (1999) Nature , vol.401 , Issue.6755 , pp. 788-791
    • Lee, D.D.1    Seung, H.S.2
  • 11
    • 85041975304 scopus 로고
    • Exploratory latent structure analysis using both identifiable and unidentifiable models
    • Goodman L. A., Exploratory latent structure analysis using both identifiable and unidentifiable models Biometrika 1974 61 2 215 231
    • (1974) Biometrika , vol.61 , Issue.2 , pp. 215-231
    • Goodman, L.A.1
  • 13
    • 0039565109 scopus 로고
    • Latent structure analysis and its relation to factor analysis
    • Green B. F. Jr., Latent structure analysis and its relation to factor analysis Journal of the American Statistical Association 1952 47 257 71 76
    • (1952) Journal of the American Statistical Association , vol.47 , Issue.257 , pp. 71-76
    • Green Jr., B.F.1
  • 26
    • 84900510076 scopus 로고    scopus 로고
    • Non-negative matrix factorization with sparseness constraints
    • Hoyer P. O., Non-negative matrix factorization with sparseness constraints The Journal of Machine Learning Research 2004 5 1457 1469
    • (2004) The Journal of Machine Learning Research , vol.5 , pp. 1457-1469
    • Hoyer, P.O.1
  • 29
    • 33646365077 scopus 로고    scopus 로고
    • For most large undetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution
    • Donoho D., For most large undetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution Communications on Pure and Applied Mathematics 2006 59 7 903 934
    • (2006) Communications on Pure and Applied Mathematics , vol.59 , Issue.7 , pp. 903-934
    • Donoho, D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.