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Volumn 8, Issue , 2007, Pages 1919-1986

A generalized maximum entropy approach to Bregman Co-clustering and matrix approximation

Author keywords

Bregman divergences; Bregman information; Co clustering; Matrix approximation; Maximum entropy

Indexed keywords

APPROXIMATION THEORY; DATA REDUCTION; ERROR ANALYSIS; INFORMATION ANALYSIS; MICROARRAYS;

EID: 34548691246     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (219)

References (50)
  • 1
    • 0035370926 scopus 로고    scopus 로고
    • Relative loss bounds for on-line density estimation with the exponential family of distributions
    • K. S. Azoury and M. K. Warmuth. Relative loss bounds for on-line density estimation with the exponential family of distributions. Machine Learning, 43(3):211-246, 2001.
    • (2001) Machine Learning , vol.43 , Issue.3 , pp. 211-246
    • Azoury, K.S.1    Warmuth, M.K.2
  • 2
    • 23744473964 scopus 로고    scopus 로고
    • On the optimality of conditional expectation as a Bregman predictor
    • July
    • A. Banerjee, X. Guo, and H. Wang. On the optimality of conditional expectation as a Bregman predictor. IEEE Transactions on Information Theory, 51(7):2664-2669, July 2005a.
    • (2005) IEEE Transactions on Information Theory , vol.51 , Issue.7 , pp. 2664-2669
    • Banerjee, A.1    Guo, X.2    Wang, H.3
  • 4
    • 21744459385 scopus 로고    scopus 로고
    • Legendre functions and the method of random Bregman projections
    • H. H. Bauschke and J. M. Borowein. Legendre functions and the method of random Bregman projections. Journal of Convex Analysis, 4(1):27-67, 1997.
    • (1997) Journal of Convex Analysis , vol.4 , Issue.1 , pp. 27-67
    • Bauschke, H.H.1    Borowein, J.M.2
  • 6
    • 49949144765 scopus 로고
    • The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
    • L. M. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Computational Mathematics and Physics, 7:200-217, 1967.
    • (1967) USSR Computational Mathematics and Physics , vol.7 , pp. 200-217
    • Bregman, L.M.1
  • 15
    • 0025595687 scopus 로고
    • Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems
    • I. Csiszár. Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems. The Annals of Statistics, 19:2032-2066, 1991.
    • (1991) The Annals of Statistics , vol.19 , pp. 2032-2066
    • Csiszár, I.1
  • 16
    • 0004027463 scopus 로고    scopus 로고
    • Duality and auxiliary functions for Bregman distances
    • Technical Report CMU-CS-01-109, School of Computer Science, Carnegie Mellon University
    • S. Della Pietra, V. Della Pietra, and J. Lafferty. Duality and auxiliary functions for Bregman distances. Technical Report CMU-CS-01-109, School of Computer Science, Carnegie Mellon University, 2001.
    • (2001)
    • Della Pietra, S.1    Della Pietra, V.2    Lafferty, J.3
  • 17
    • 2942723846 scopus 로고    scopus 로고
    • A divisive information-theoretic feature clustering algorithm for text classification
    • I. Dhillon, S. Mallela, and R. Kumar. A divisive information-theoretic feature clustering algorithm for text classification. Journal of Machine Learning Research, 3(4): 1265-1287, 2003a.
    • (2003) Journal of Machine Learning Research , vol.3 , Issue.4 , pp. 1265-1287
    • Dhillon, I.1    Mallela, S.2    Kumar, R.3
  • 20
    • 0034824884 scopus 로고    scopus 로고
    • Concept decompositions for large sparse text data using clustering
    • January
    • I. S. Dhillon and D. S. Modha. Concept decompositions for large sparse text data using clustering. Machine Learning, 42(1): 143-175, January 2001.
    • (2001) Machine Learning , vol.42 , Issue.1 , pp. 143-175
    • Dhillon, I.S.1    Modha, D.S.2
  • 21
    • 33645103900 scopus 로고    scopus 로고
    • Trained named entity recognition using distributional clusters
    • D. Freitag. Trained named entity recognition using distributional clusters. In EMNLP, pages 262-269, 2004.
    • (2004) EMNLP , pp. 262-269
    • Freitag, D.1
  • 24
    • 2542613410 scopus 로고    scopus 로고
    • Scalable clustering
    • Nong Ye, editor, Lawrence Erlbaum Assoc
    • J. Ghosh. Scalable clustering. In Nong Ye, editor, The Handbook of Data Mining, pages 247-277. Lawrence Erlbaum Assoc., 2003.
    • (2003) The Handbook of Data Mining , pp. 247-277
    • Ghosh, J.1
  • 25
    • 34548695058 scopus 로고    scopus 로고
    • data set
    • GroupLens. Movielens data set. http://www.cs.umn.edu/Research/GroupLens/ data/ml-data.tar.gz.
    • Movielens
    • GroupLens1
  • 26
    • 6344274901 scopus 로고    scopus 로고
    • Game theory, maximum entropy, minimum discrepancy, and robust Bayesian decision theory
    • P. D. Grünwald and A. Dawid. Game theory, maximum entropy, minimum discrepancy, and robust Bayesian decision theory. Annals of Statistics, 32(4), 2004.
    • (2004) Annals of Statistics , vol.32 , Issue.4
    • Grünwald, P.D.1    Dawid, A.2
  • 27
    • 42749107856 scopus 로고    scopus 로고
    • Spectral images and features co-clustering with application to content-based image retrieval
    • J. Guan, G. Qiu, and X. Y. Xue. Spectral images and features co-clustering with application to content-based image retrieval. In IEEE Workshop on Multimedia Signal Processing, 2005.
    • (2005) IEEE Workshop on Multimedia Signal Processing
    • Guan, J.1    Qiu, G.2    Xue, X.Y.3
  • 29
    • 3042742744 scopus 로고    scopus 로고
    • Latent semantic models for collaborative filtering
    • T. Hofmann. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 22(1):89-115, 2004.
    • (2004) ACM Transactions on Information Systems , vol.22 , Issue.1 , pp. 89-115
    • Hofmann, T.1
  • 30
    • 0344031459 scopus 로고    scopus 로고
    • Unsupervised learning from dyadic data
    • TR-98-042, International Computer Science Institute ICSI, Berkeley
    • T. Hofmann and J. Puzicha. Unsupervised learning from dyadic data. Technical Report ICSI TR-98-042, International Computer Science Institute (ICSI), Berkeley, 1998.
    • (1998) Technical Report ICSI
    • Hofmann, T.1    Puzicha, J.2
  • 32
    • 11944266539 scopus 로고
    • Information theory and statistical mechanics
    • E. T. Jaynes. Information theory and statistical mechanics. Physical Reviews, 106:620-630, 1957.
    • (1957) Physical Reviews , vol.106 , pp. 620-630
    • Jaynes, E.T.1
  • 33
    • 0037399130 scopus 로고    scopus 로고
    • Spectral biclustering of microarray data: Coclustering genes and conditions
    • Y. Kluger, R. Basri, J. T. Chang, and M. Gerstein. Spectral biclustering of microarray data: Coclustering genes and conditions. Genome Research, 13(4):703-716, 2003.
    • (2003) Genome Research , vol.13 , Issue.4 , pp. 703-716
    • Kluger, Y.1    Basri, R.2    Chang, J.T.3    Gerstein, M.4
  • 36
    • 85149113738 scopus 로고    scopus 로고
    • Word clustering and disambiguation based on co-occurence data
    • H. Li and N. Abe. Word clustering and disambiguation based on co-occurence data. In COLING-ACL, pages 749-755, 1998.
    • (1998) COLING-ACL , pp. 749-755
    • Li, H.1    Abe, N.2
  • 38
    • 6344233909 scopus 로고    scopus 로고
    • Finding haplotype tagging snps by use of principal components analysis
    • Z. Lin and R.B. Altman. Finding haplotype tagging snps by use of principal components analysis. The American Journal of Human Genetics, 75:850-861, 2004.
    • (2004) The American Journal of Human Genetics , vol.75 , pp. 850-861
    • Lin, Z.1    Altman, R.B.2
  • 41
    • 17044376078 scopus 로고    scopus 로고
    • Subspace clustering for high dimensinal data: A review
    • L. Parsons, E. Haque, and H. Liu. Subspace clustering for high dimensinal data: A review. ACM SIGKDD Explorations, 6(1):90-105, 2004.
    • (2004) ACM SIGKDD Explorations , vol.6 , Issue.1 , pp. 90-105
    • Parsons, L.1    Haque, E.2    Liu, H.3
  • 44
    • 34548698558 scopus 로고    scopus 로고
    • R. T. Rockafellar. Convex Analysis. Princeton Landmarks in Mathematics. Princeton University Press, 1970.
    • R. T. Rockafellar. Convex Analysis. Princeton Landmarks in Mathematics. Princeton University Press, 1970.
  • 46
    • 0012972602 scopus 로고    scopus 로고
    • Application of dimensionality reduction in recommender systems - a case study
    • B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Application of dimensionality reduction in recommender systems - a case study. In WebKDD Workshop., 2000.
    • (2000) WebKDD Workshop
    • Sarwar, B.1    Karypis, G.2    Konstan, J.3    Riedl, J.4
  • 47
    • 0018877134 scopus 로고
    • Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross entropy
    • J. Shore and R. Johnson. Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross entropy. IEEE Transactions on Information Theory, 26(1):26-37, 1980.
    • (1980) IEEE Transactions on Information Theory , vol.26 , Issue.1 , pp. 26-37
    • Shore, J.1    Johnson, R.2
  • 48
    • 0041965980 scopus 로고    scopus 로고
    • Cluster ensembles - a knowledge reuse framework for combining partitionings
    • A. Strehl and J. Ghosh. Cluster ensembles - a knowledge reuse framework for combining partitionings. Journal of Machine Learning Research, 3(3):583-617, 2002.
    • (2002) Journal of Machine Learning Research , vol.3 , Issue.3 , pp. 583-617
    • Strehl, A.1    Ghosh, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.