{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:16:46Z","timestamp":1771024606750,"version":"3.50.1"},"reference-count":8,"publisher":"MIT Press","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neural Computation"],"published-print":{"date-parts":[[2000,9,1]]},"abstract":"<jats:p>We present a split-and-merge expectation-maximization (SMEM) algorithm to overcome the local maxima problem in parameter estimation of finite mixture models. In the case of mixture models, local maxima often involve having too many components of a mixture model in one part of the space and too few in another, widely separated part of the space. To escape from such configurations, we repeatedly perform simultaneous split-and-merge operations using a new criterion for efficiently selecting the split-and-merge candidates. We apply the proposed algorithm to the training of gaussian mixtures and mixtures of factor analyzers using synthetic and real data and show the effectiveness of using the split- and-merge operations to improve the likelihood of both the training data and of held-out test data. We also show the practical usefulness of the proposed algorithm by applying it to image compression and pattern recognition problems.<\/jats:p>","DOI":"10.1162\/089976600300015088","type":"journal-article","created":{"date-parts":[[2002,7,27]],"date-time":"2002-07-27T11:56:30Z","timestamp":1027770990000},"page":"2109-2128","source":"Crossref","is-referenced-by-count":266,"title":["SMEM Algorithm for Mixture Models"],"prefix":"10.1162","volume":"12","author":[{"given":"Naonori","family":"Ueda","sequence":"first","affiliation":[{"name":"NTT Communication Science Laboratories, Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237 Japan"}]},{"given":"Ryohei","family":"Nakano","sequence":"additional","affiliation":[{"name":"NTT Communication Science Laboratories, Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237 Japan"}]},{"given":"Zoubin","family":"Ghahramani","sequence":"additional","affiliation":[{"name":"Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, U.K."}]},{"given":"Geoffrey E.","family":"Hinton","sequence":"additional","affiliation":[{"name":"Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, U.K."}]}],"member":"281","reference":[{"key":"p_2","doi-asserted-by":"publisher","DOI":"10.1002\/bs.3830120210"},{"key":"p_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"Dempster A. P.","year":"1977","journal-title":"Journal of Royal Statistical Society B"},{"issue":"1","key":"p_5","first-page":"65","volume":"8","author":"Hinton G. E.","year":"1997","journal-title":"IEEE Trans. PAMI"},{"issue":"9","key":"p_6","first-page":"669","volume":"21","author":"Ishii K.","year":"1989","journal-title":"Systems and Computers in Japan"},{"key":"p_10","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00095"},{"key":"p_13","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(94)90003-5"},{"key":"p_14","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(97)00133-0"},{"key":"p_16","doi-asserted-by":"publisher","DOI":"10.1207\/s15327906mbr0503_6"}],"container-title":["Neural Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mitpressjournals.org\/doi\/pdf\/10.1162\/089976600300015088","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T23:50:42Z","timestamp":1733701842000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/neco\/article\/12\/9\/2109-2128\/6442"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2000,9,1]]},"references-count":8,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2000,9,1]]}},"alternative-id":["10.1162\/089976600300015088"],"URL":"https:\/\/doi.org\/10.1162\/089976600300015088","relation":{},"ISSN":["0899-7667","1530-888X"],"issn-type":[{"value":"0899-7667","type":"print"},{"value":"1530-888X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2000,9,1]]}}}