메뉴 건너뛰기




Volumn 12, Issue 1, 2009, Pages 55-78

The aspect Bernoulli model: Multiple causes of presences and absences

Author keywords

0 1 data; Data mining; Multiple cause models; Probabilistic latent variable models

Indexed keywords


EID: 58849140543     PISSN: 14337541     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10044-007-0096-4     Document Type: Article
Times cited : (28)

References (63)
  • 1
    • 0003406531 scopus 로고
    • 2 University of Chicago Press Chicago
    • Harman HH (1967) Modern factor analysis, 2nd edn. University of Chicago Press, Chicago
    • (1967) Modern Factor Analysis
    • Harman, H.H.1
  • 2
    • 0003040479 scopus 로고
    • A multiple cause mixture model for unsupervised learning
    • E Saund 1995 A multiple cause mixture model for unsupervised learning Neural Comput 7 51 71
    • (1995) Neural Comput , vol.7 , pp. 51-71
    • Saund, E.1
  • 3
    • 0034818212 scopus 로고    scopus 로고
    • Unsupervised learning by probabilistic latent semantic analysis
    • T Hofmann 2001 Unsupervised learning by probabilistic latent semantic analysis Mach Learn 42 177 196
    • (2001) Mach Learn , vol.42 , pp. 177-196
    • Hofmann, T.1
  • 7
    • 84945258136 scopus 로고    scopus 로고
    • Variational extensions to em and multinomial PCA
    • Machine learning: ECML 2002 Springer, New York
    • Buntine W (2002) Variational extensions to EM and multinomial PCA. In: Machine learning: ECML 2002. Lecture notes in artificial intelligence (LNAI), vol 2430. Springer, New York, pp 23-34
    • (2002) Lecture Notes in Artificial Intelligence (LNAI) , vol.2430 , pp. 23-34
    • Buntine, W.1
  • 8
    • 0001673996 scopus 로고    scopus 로고
    • A comparison of event models for naive Bayes text classification
    • Sahami M (ed) Learning for text categorization Technical report WS-98-05, AAAI
    • McCallum A, Nigam K (1998) A comparison of event models for naive Bayes text classification. In: Sahami M (ed) Learning for text categorization. Papers from the AAAI Workshop, Technical report WS-98-05, AAAI, pp 41-48
    • (1998) AAAI Workshop , pp. 41-48
    • McCallum, A.1    Nigam, K.2
  • 11
    • 0344031459 scopus 로고    scopus 로고
    • Unsupervised learning from dyadic data
    • International Computer Science Insitute, Berkeley
    • Hofmann T, Puzicha J (1998) Unsupervised learning from dyadic data. Technical Report TR-98-042, International Computer Science Insitute, Berkeley
    • (1998) Technical Report TR-98-042 , vol.TR-98-042
    • Hofmann, T.1    Puzicha, J.2
  • 12
    • 3042742744 scopus 로고    scopus 로고
    • Latent semantic models for collaborative filtering
    • T Hofmann 2004 Latent semantic models for collaborative filtering ACM Trans Inf Syst 22 89 115
    • (2004) ACM Trans Inf Syst , vol.22 , pp. 89-115
    • Hofmann, T.1
  • 16
    • 33745711564 scopus 로고    scopus 로고
    • ICA-based binary feature construction
    • Rosca JP, Erdogmus D, Príncipe JC, Haykin S (eds) Independent component analysis and blind signal separation. 6th international conference, ICA 2006, Proceedings Springer, Berlin
    • Kabán A, Bingham E (2006) ICA-based binary feature construction. In: Rosca JP, Erdogmus D, Príncipe JC, Haykin S (eds) Independent component analysis and blind signal separation. 6th international conference, ICA 2006, Proceedings. Lecture notes in computer science, vol 3889. Springer, Berlin, pp 140-148
    • (2006) Lecture Notes in Computer Science , vol.3889 , pp. 140-148
    • Kabán Bingham, A.E.1
  • 19
    • 21344497321 scopus 로고
    • Non-uniqueness in probabilistic numerical identification of bacteria
    • M Gyllenberg T Koski E Reilink M Verlaan 1994 Non-uniqueness in probabilistic numerical identification of bacteria J Appl Probab 31 542 548
    • (1994) J Appl Probab , vol.31 , pp. 542-548
    • Gyllenberg, M.1    Koski, T.2    Reilink, E.3    Verlaan, M.4
  • 20
    • 34547093747 scopus 로고    scopus 로고
    • An accelerated Chow and Liu algorithm: Fitting tree distributions to high-dimensional sparse data
    • Morgan Kaufmann, San Francisco
    • Meilǎ M (1999) An accelerated Chow and Liu algorithm: fitting tree distributions to high-dimensional sparse data. In: ICML '99: Proceedings of the sixteenth international conference on machine learning, Morgan Kaufmann, San Francisco, pp 249-257
    • (1999) ICML '99: Proceedings of the Sixteenth International Conference on Machine Learning , pp. 249-257
    • Meilǎ, M.1
  • 22
    • 84899007505 scopus 로고    scopus 로고
    • Probabilistic visualisation of high-dimensional binary data
    • Kearns MS, Solla SA, Cohn DA (eds)
    • Tipping ME (1999) Probabilistic visualisation of high-dimensional binary data. In: Kearns MS, Solla SA, Cohn DA (eds) Advances in neural information processing systems, vol 11, pp 592-598
    • (1999) Advances in Neural Information Processing Systems , vol.11 , pp. 592-598
    • Tipping, M.E.1
  • 25
    • 0001179408 scopus 로고
    • Competition and multiple cause models
    • P Dayan RS Zemel 1995 Competition and multiple cause models Neural Comput 7 565 579
    • (1995) Neural Comput , vol.7 , pp. 565-579
    • Dayan, P.1    Zemel, R.S.2
  • 26
    • 9444241025 scopus 로고    scopus 로고
    • A simple algorithm for topic identification in 0-1 data
    • Lavrač N, Gamberger D, Todorovski L, Blockeel H (eds) Knowledge discovery in databases: PKDD 2003 Springer, New York
    • Seppänen JK, Bingham E, Mannila H (2003) A simple algorithm for topic identification in 0-1 data. In: Lavrač N, Gamberger D, Todorovski L, Blockeel H (eds) Knowledge discovery in databases: PKDD 2003. Lecture notes in artificial intelligence, vol 2832, Springer, New York, pp 423-434
    • (2003) Lecture Notes in Artificial Intelligence , vol.2832 , pp. 423-434
    • Seppänen, J.K.1    Bingham, E.2    Mannila, H.3
  • 29
    • 0025604930 scopus 로고
    • Forming sparse representations by local anti-Hebbian learning
    • P Földiák 1990 Forming sparse representations by local anti-Hebbian learning Biol Cybern 64 165 170
    • (1990) Biol Cybern , vol.64 , pp. 165-170
    • Földiák, P.1
  • 30
    • 0040422903 scopus 로고
    • Learning factorial codes by predictability minimization
    • J Schmidhuber 1992 Learning factorial codes by predictability minimization Neural Comput 4 863 879
    • (1992) Neural Comput , vol.4 , pp. 863-879
    • Schmidhuber, J.1
  • 32
    • 34548582878 scopus 로고    scopus 로고
    • Discovering latent classes in relational data
    • Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory
    • Kemp C, Griffiths TL, Tenenbaum JB (2004) Discovering latent classes in relational data. Technical Report AI Memo 2004-019, Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory
    • (2004) Technical Report AI Memo , vol.2004 , Issue.19
    • Kemp, C.1    Griffiths, T.L.2    Tenenbaum, J.B.3
  • 34
    • 0007825802 scopus 로고    scopus 로고
    • Co-clustering documents and words using bipartite spectral graph partitioning
    • Department of Computer Sciences, University of Texas, Austin
    • Dhillon IS (2001) Co-clustering documents and words using bipartite spectral graph partitioning. Technical Report TR 2001-05, Department of Computer Sciences, University of Texas, Austin
    • Technical Report , vol.2001 TR 2001-05
    • Dhillon, I.S.1
  • 37
    • 0028561099 scopus 로고
    • Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values
    • P Paatero U Tapper 1994 Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values Environmetrics 5 111 126
    • (1994) Environmetrics , vol.5 , pp. 111-126
    • Paatero, P.1    Tapper, U.2
  • 38
    • 0033592606 scopus 로고    scopus 로고
    • Learning the parts of objects by non-negative matrix factorization
    • DD Lee HS Seung 1999 Learning the parts of objects by non-negative matrix factorization Nature 401 788 791
    • (1999) Nature , vol.401 , pp. 788-791
    • Lee, D.D.1    Seung, H.S.2
  • 41
    • 12844275138 scopus 로고    scopus 로고
    • A Bayesian approach to object detection using probabilistic appearance-based models
    • R Dahyot P Charbonnier F Heitz 2004 A Bayesian approach to object detection using probabilistic appearance-based models Pattern Anal Appl 7 317 332
    • (2004) Pattern Anal Appl , vol.7 , pp. 317-332
    • Dahyot, R.1    Charbonnier, P.2    Heitz, F.3
  • 46
    • 33645797854 scopus 로고    scopus 로고
    • Seriation in paleontological data using Markov chain Monte Carlo methods
    • K Puolamäki M Fortelius H Mannila 2006 Seriation in paleontological data using Markov chain Monte Carlo methods PLoS Comput Biol 2 e6
    • (2006) PLoS Comput Biol , vol.2 , pp. 6
    • Puolamäki, K.1    Fortelius, M.2    Mannila, H.3
  • 48
    • 0001942153 scopus 로고    scopus 로고
    • Model selection for probabilistic clustering using cross-validated likelihood
    • P Smyth 2000 Model selection for probabilistic clustering using cross-validated likelihood Stat Comput 10 63 72
    • (2000) Stat Comput , vol.10 , pp. 63-72
    • Smyth, P.1
  • 49
    • 0003602554 scopus 로고    scopus 로고
    • A comparison of scientific and engineering criteria for Bayesian model selection
    • Microsoft Research
    • Heckerman D, Chickering DM (1996) A comparison of scientific and engineering criteria for Bayesian model selection. Technical Report MSR-TR-96-12, Microsoft Research
    • (1996) Technical Report , vol.MSR-TR-96-12
    • Heckerman, D.1    Chickering, D.M.2
  • 51
    • 0000501656 scopus 로고
    • Information theory and an extension of the maximum likelihod principle
    • Petrox B, Csaki F (eds)
    • Akaike H (1973) Information theory and an extension of the maximum likelihod principle. In: Petrox B, Csaki F (eds) Second international symposium on information theory, pp 267-281
    • (1973) Second International Symposium on Information Theory , pp. 267-281
    • Akaike, H.1
  • 52
    • 34249684237 scopus 로고    scopus 로고
    • Predictive modelling of heterogeneous sequence collections by topographic ordering of histories
    • A Kabán 2007 Predictive modelling of heterogeneous sequence collections by topographic ordering of histories Mach Learn 68 63 95
    • (2007) Mach Learn , vol.68 , pp. 63-95
    • Kabán, A.1
  • 53
    • 0002259425 scopus 로고
    • A new method for mapping optimization problems onto neural networks
    • C Peterson B Söderberg 1989 A new method for mapping optimization problems onto neural networks Int J Neural Syst 1 3 22
    • (1989) Int J Neural Syst , vol.1 , pp. 3-22
    • Peterson, C.1    Söderberg, B.2
  • 54
    • 0035273462 scopus 로고    scopus 로고
    • Independent component analysis using Potts models
    • J-M Wu S-J Chiu 2001 Independent component analysis using Potts models IEEE Trans Neural Netw 12 202 211
    • (2001) IEEE Trans Neural Netw , vol.12 , pp. 202-211
    • Wu, J.-M.1    Chiu, S.-J.2
  • 56
    • 0034730140 scopus 로고    scopus 로고
    • Singular value decomposition for genome-wide expression data processing and modeling
    • O Alter PO Brown D Botstein 2000 Singular value decomposition for genome-wide expression data processing and modeling PNAS 97 10101 10106
    • (2000) PNAS , vol.97 , pp. 10101-10106
    • Alter, O.1    Brown, P.O.2    Botstein, D.3
  • 58
    • 84880663504 scopus 로고    scopus 로고
    • The cluster-abstraction model: Unsupervised learning of topic hierarchies from text data
    • Dean T (ed) Morgan Kaufmann, San Francisco
    • Hofmann T (1999) The cluster-abstraction model: unsupervised learning of topic hierarchies from text data. In: Dean T (ed) Proceedings of the 16th international joint conference on artificial intelligence, IJCAI 99. Morgan Kaufmann, San Francisco, pp 682-687
    • (1999) Proceedings of the 16th International Joint Conference on Artificial Intelligence, IJCAI 99 , pp. 682-687
    • Hofmann, T.1
  • 62
    • 26444565692 scopus 로고    scopus 로고
    • Finding uninformative features in binary data
    • Gallagher M, Hogan JM, Maire F (eds) Intelligent data engineering and automated learning-IDEAL 2005 Springer, New York
    • Wang X, Kabán A (2005) Finding uninformative features in binary data. In: Gallagher M, Hogan JM, Maire F (eds) Intelligent data engineering and automated learning-IDEAL 2005. Lecture notes in computer science, vol 3578. Springer, New York, pp 40-47
    • (2005) Lecture Notes in Computer Science , vol.3578 , pp. 40-47
    • Wang Kabán, X.A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.