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Volumn , Issue , 2008, Pages 819-820

Author-topic evolution analysis using three-way non-negative Paratucker

Author keywords

Author topic evolution; Tensor; Three way data

Indexed keywords

AUTHOR-TOPIC EVOLUTION; DATA SETS; PARAFAC; RICH STRUCTURES; TEMPORAL RELATIONS; THREE-WAY DATA; TOPIC EVOLUTIONS;

EID: 57349156163     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390334.1390521     Document Type: Conference Paper
Times cited : (5)

References (9)
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    • H. Cho, I. Dhillon, Y. Guan, and S. Sra. Minimum sum squared residue co-clustering of gene expression data. In Proceedings of SIAM'04, pages 22-24, 2004.
    • (2004) Proceedings of SIAM'04 , pp. 22-24
    • Cho, H.1    Dhillon, I.2    Guan, Y.3    Sra, S.4
  • 5
    • 0030521152 scopus 로고    scopus 로고
    • Uniqueness proof for a family of models sharing features of tucker's three-mode factor analysis and parafac/candecomp
    • R. Harshman and M. Lundy. Uniqueness proof for a family of models sharing features of tucker's three-mode factor analysis and parafac/candecomp. Psychometrika, 61(1): 133-154, 1996.
    • (1996) Psychometrika , vol.61 , Issue.1 , pp. 133-154
    • Harshman, R.1    Lundy, M.2
  • 6
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • D. D. Lee and S. H. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788-791, 1999.
    • (1999) Nature , vol.401 , Issue.6755 , pp. 788-791
    • Lee, D.D.1    Seung, S.H.2
  • 7
    • 57349114307 scopus 로고    scopus 로고
    • R.A.Harshman. Foundations of the parafac procedure: models and conditions for an 'explanatory' multi-modal factor analysis. UCLA working papers in phonetics 16, pages 1.-84, 1970.
    • R.A.Harshman. Foundations of the parafac procedure: models and conditions for an 'explanatory' multi-modal factor analysis. UCLA working papers in phonetics 16, pages 1.-84, 1970.
  • 8
    • 0041965980 scopus 로고    scopus 로고
    • Cluster ensembles-a knowledge reuse framework for combining multiple partitions
    • A. Strehl and J. Ghosh. Cluster ensembles-a knowledge reuse framework for combining multiple partitions. Journal on Machine Learning Research. 3:583-617, 2002.
    • (2002) Journal on Machine Learning Research , vol.3 , pp. 583-617
    • Strehl, A.1    Ghosh, J.2
  • 9
    • 33749575326 scopus 로고    scopus 로고
    • Orthogonal nonnegative matrix t-factorizations for clustering
    • C. Ding, T. Li, W. Peng, and H. Park. Orthogonal nonnegative matrix t-factorizations for clustering Proceedings of KDD'06, pages 126-135, 2006.
    • (2006) Proceedings of KDD'06 , pp. 126-135
    • Ding, C.1    Li, T.2    Peng, W.3    Park, H.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.