{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:11:01Z","timestamp":1761581461354,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,8,26]],"date-time":"2016-08-26T00:00:00Z","timestamp":1472169600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"CTU in Prague","award":["10\/279\/OHK3\/3T\/13"],"award-info":[{"award-number":["10\/279\/OHK3\/3T\/13"]}]},{"name":"Ministry of Education, Youth and Sports of the Czech Republic","award":["MSM6840770012"],"award-info":[{"award-number":["MSM6840770012"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2018,2]]},"DOI":"10.1007\/s10044-016-0576-5","type":"journal-article","created":{"date-parts":[[2016,8,26]],"date-time":"2016-08-26T06:38:47Z","timestamp":1472193527000},"page":"181-192","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Estimating number of components in Gaussian mixture model using combination of greedy and merging algorithm"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4239-2092","authenticated-orcid":false,"given":"Karla","family":"\u0160tep\u00e1nov\u00e1","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michal","family":"Vavre\u010dka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,8,26]]},"reference":[{"issue":"4","key":"576_CR1","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1162\/089892903321662976","volume":"15","author":"M Bar","year":"2003","unstructured":"Bar M (2003) A cortical mechanism for triggering top-down facilitation in visual object recognition. J Cogn Neurosci 15(4):600\u2013609","journal-title":"J Cogn Neurosci"},{"issue":"11","key":"576_CR2","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1038\/nrn1534","volume":"5","author":"DL Schacter","year":"2004","unstructured":"Schacter DL, Dobbins IG, Schnyer DM (2004) Specificity of priming: a cognitive neuroscience perspective. Nat Rev Neurosci 5(11):853\u2013862","journal-title":"Nat Rev Neurosci"},{"issue":"1481","key":"576_CR3","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1098\/rstb.2007.2087","volume":"362","author":"DL Schacter","year":"2007","unstructured":"Schacter DL, Addis DR (2007) The cognitive neuroscience of constructive memory: remembering the past and imagining the future. Philos Trans R Soc B Biol Sci 362(1481):773\u2013786","journal-title":"Philos Trans R Soc B Biol Sci"},{"key":"576_CR4","volume-title":"Neural neworks and intellect: using model-based concepts","author":"LI Perlovsky","year":"2001","unstructured":"Perlovsky LI (2001) Neural neworks and intellect: using model-based concepts. Oxford University Press, New York"},{"key":"576_CR5","series-title":"Studies in computational intelligence","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-22830-8","volume-title":"Emotional cognitive neural algorithms with engineering applications","author":"LI Perlovsky","year":"2011","unstructured":"Perlovsky LI, Deming R, Illin R (2011) Emotional cognitive neural algorithms with engineering applications. Studies in computational intelligence. Springer, Berlin"},{"key":"576_CR6","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1093\/comjnl\/41.8.578","volume":"41","author":"Ch Fraley","year":"1998","unstructured":"Fraley Ch, Raftery AE (1998) How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput J 41:578\u2013588","journal-title":"Comput J"},{"key":"576_CR7","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1109\/TNN.2009.2025501","volume":"20","author":"LI Perlovsky","year":"2009","unstructured":"Perlovsky LI (2009) Vague-to-crisp? Neural mechanism of perception. IEEE Trans Neural Netw 20:1363\u20131367","journal-title":"IEEE Trans Neural Netw"},{"issue":"2007","key":"576_CR8","first-page":"363","volume":"209","author":"LI Perlovsky","year":"2007","unstructured":"Perlovsky LI (2007) Neural networks, fuzzy models and dynamic logic. Asp Autom Text Anal 209(2007):363\u2013386","journal-title":"Asp Autom Text Anal"},{"issue":"1","key":"576_CR9","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/0893-6080(91)90035-4","volume":"4","author":"LI Perlovsky","year":"1991","unstructured":"Perlovsky LI, McManus MM (1991) Maximum likelihood neural networks for sensor fusion and adaptive classification. Neural Netw 4(1):89\u2013102","journal-title":"Neural Netw"},{"key":"576_CR10","doi-asserted-by":"crossref","unstructured":"Perlovsky LI (2005) Neural network with fuzzy dynamic logic. In: Proceedings of international joint conference on neural network, pp 3046\u20133051","DOI":"10.1109\/IJCNN.2005.1556411"},{"key":"576_CR11","unstructured":"Deming R, Perlovsky LI (2008) Multi-target\/multi-sensor tracking from optical data using modelling field theory. In: World congress on computational intelligence (WCCI). Hong Kong, China"},{"issue":"3","key":"576_CR12","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/MCI.2007.385366","volume":"2","author":"A Cangelosi","year":"2007","unstructured":"Cangelosi A, Tikhanoff V, Fontanari JF (2007) Integrating language and cognition: a cognitive robotics approach. Comput Intell Mag 2(3):65\u201370","journal-title":"Comput Intell Mag"},{"issue":"3","key":"576_CR13","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1109\/34.990138","volume":"24","author":"MAT Figueiredo","year":"2002","unstructured":"Figueiredo MAT, Jain AK (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 24(3):381\u2013396","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"576_CR14","doi-asserted-by":"crossref","unstructured":"Ueda N, Nakano R, Ghahramani Z, Hinton GE (1998) Split and merge EM algorithm for improving Gaussian mixture density estimates. In: Proceedings of IEEE workshop neural networks for signal processing, pp 274\u2013283","DOI":"10.1109\/NNSP.1998.710657"},{"key":"576_CR15","doi-asserted-by":"crossref","unstructured":"Li Y, Li L (2009) A novel split and merge EM algorithm for Gaussian mixture model. In: ICNC \u201909. Fifth international conference on natural computation, pp 479\u2013483","DOI":"10.1109\/ICNC.2009.625"},{"issue":"2","key":"576_CR16","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1162\/089976603762553004","volume":"5","author":"J Verbeek","year":"2003","unstructured":"Verbeek J, Vlassis M, Kr\u00f6se B (2003) Efficient greedy learning for Gaussian mixture models. Neural Comput 5(2):469\u2013485","journal-title":"Neural Comput"},{"key":"576_CR17","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1023\/A:1013844811137","volume":"15","author":"N Vlassis","year":"2002","unstructured":"Vlassis N, Likas A (2002) A greedy EM algorithm for Gaussian mixture learning. Neural Process Lett 15:77\u201387","journal-title":"Neural Process Lett"},{"issue":"6","key":"576_CR18","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","volume":"19","author":"H Akaike","year":"1974","unstructured":"Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716","journal-title":"IEEE Trans Autom Control"},{"issue":"2","key":"576_CR19","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1214\/aos\/1176344136","volume":"6","author":"GE Schwarz","year":"1978","unstructured":"Schwarz GE (1978) Estimating the dimension of a model. Ann Stat 6(2):461\u2013464","journal-title":"Ann Stat"},{"issue":"4","key":"576_CR20","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1109\/TPAMI.2006.82","volume":"28","author":"G Bouchard","year":"2006","unstructured":"Bouchard G, Celeux G (2006) Selection of generative models in classification. IEEE Trans Pattern Anal Mach Intell 28(4):544\u2013554","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"576_CR21","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/BF01246098","volume":"13","author":"G Celeux","year":"1994","unstructured":"Celeux G, Soromenho G (1994) An entropy criterion for assessing the number of clusters in a mixture models. J Classif 13:195\u2013212","journal-title":"J Classif"},{"issue":"1998","key":"576_CR22","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1007\/BFb0033308","volume":"1451","author":"H Tenmoto","year":"1998","unstructured":"Tenmoto H, Kudo M, Shimbo M (1998) MDL-based selection of the number of components in mixture models for pattern classification. Adv Pattern Recognit 1451(1998):831\u2013836","journal-title":"Adv Pattern Recognit"},{"issue":"7","key":"576_CR23","first-page":"1119","volume":"12","author":"Y Lee","year":"2006","unstructured":"Lee Y, Lee KY, Lee J (2006) The estimating optimal number of GMM based on incremental k-means for speaker identifications. Int J Inf Technol 12(7):1119\u20131128","journal-title":"Int J Inf Technol"},{"key":"576_CR24","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/S0031-3203(02)00060-2","volume":"36","author":"A Likas","year":"2003","unstructured":"Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recognit 36:451\u2013461","journal-title":"Pattern Recognit"},{"issue":"3","key":"576_CR25","doi-asserted-by":"crossref","first-page":"318","DOI":"10.2307\/2347790","volume":"36","author":"J Grim","year":"1987","unstructured":"Grim J, Novovicova J, Pudil P, Somol P, Ferri F (1987) Initialization normal mixutres of densities. Appl Stat 36(3):318\u2013324","journal-title":"Appl Stat"},{"key":"576_CR26","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1023\/A:1008940618127","volume":"9","author":"P Smyth","year":"2000","unstructured":"Smyth P (2000) Model selection for probabilistic clustering using crossvalidated likelihood. Stat Comput 9:63","journal-title":"Stat Comput"},{"issue":"3","key":"576_CR27","doi-asserted-by":"crossref","first-page":"318","DOI":"10.2307\/2347790","volume":"36","author":"G McLachlan","year":"1987","unstructured":"McLachlan G (1987) On bootstraping the likelihood ratio test statistic for the number of components in a normal mixture. Appl Stat 36(3):318\u2013324","journal-title":"Appl Stat"},{"issue":"8","key":"576_CR28","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1109\/TPAMI.2005.162","volume":"27","author":"F Pernkopf","year":"2005","unstructured":"Pernkopf F, Bouchaffra D (2005) Genetic-based EM algorithm for learning gaussian mixture models. IEEE Trans Pattern Anal Mach Intell 27(8):1344\u20131348","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"7B","key":"576_CR29","doi-asserted-by":"crossref","first-page":"2797","DOI":"10.1109\/TSP.2008.917350","volume":"56","author":"D Ververidis","year":"2008","unstructured":"Ververidis D, Kotropoulos C (2008) Gaussian mixture modeling by exploiting the Mahalanobis distance. IEEE Trans Signal Process 56(7B):2797\u20132811","journal-title":"IEEE Trans Signal Process"},{"key":"576_CR30","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/BF02481962","volume":"36","author":"H Bozdogan","year":"1984","unstructured":"Bozdogan H, Sclove SL (1984) Multi-sample cluster analysis using Akaike\u2019s information criterion. Ann Inst Stat Math 36:163\u2013180","journal-title":"Ann Inst Stat Math"},{"issue":"11","key":"576_CR31","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1109\/34.730550","volume":"20","author":"S Roberts","year":"1998","unstructured":"Roberts S, Husmaier D, Rezek I, Penny W (1998) Bayesian approaches to Gaussian mixture modelling. IEEE Trans Pattern Anal Mach Intell 20(11):1133\u20131142","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"576_CR32","doi-asserted-by":"crossref","first-page":"803","DOI":"10.2307\/2532201","volume":"49","author":"J Banfield","year":"1993","unstructured":"Banfield J, Raftery A (1993) Model-based Gaussian and non-Gaussian clustering. Biometrics 49:803\u2013821","journal-title":"Biometrics"},{"key":"576_CR33","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1007\/978-3-642-50974-2_5","volume-title":"Information and Classification","author":"H Bozdogan","year":"1993","unstructured":"Bozdogan H (1993) Choosing the number of component clusters in the mixture model using a new informational complexity criterion of the inverse Fisher information matrix. In: Opitz O, Lausen B, Klar R (eds) Information and Classification, Springer-Verlag, Berlin, pp 40\u201354"},{"key":"576_CR34","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1080\/01621459.1992.10476277","volume":"87","author":"M Whindham","year":"1992","unstructured":"Whindham M, Cutler A (1992) Information ratios for validating mixture analysis. J Am Stat Assoc 87:1188\u20131192","journal-title":"J Am Stat Assoc"},{"issue":"3","key":"576_CR35","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0167-8655(98)00144-5","volume":"20","author":"C Biernacki","year":"1999","unstructured":"Biernacki C, Celeux G (1999) An improvement of the NEC criterion for assessing the number of clusters in a mixture model. Pattern Recognit Lett 20(3):267\u2013272","journal-title":"Pattern Recognit Lett"},{"key":"576_CR36","first-page":"364","volume-title":"Proceedings of the Thirteenth International Conference (ICML96)","author":"JJ Oliver","year":"1996","unstructured":"Oliver JJ, Baxter RA, Wallace CS (1996) Unsupervised Learning using MML. In: Proceedings of the Thirteenth International Conference (ICML96), Morgan Kaufmann Publishers, SanFrancisco, CA, pp 364\u2013372"},{"key":"576_CR37","volume-title":"Stochastic complexity in statistical inquiry","author":"J Rissanen","year":"1989","unstructured":"Rissanen J (1989) Stochastic complexity in statisticalinquiry. World Scientific, Singapore"},{"issue":"2","key":"576_CR38","first-page":"451","volume":"29","author":"C Biernacki","year":"1997","unstructured":"Biernacki C, Govaert G (1997) Using the classification likelihood to choose the number of clusters. Comput sci stat 29(2):451\u2013457","journal-title":"Comput sci stat"},{"issue":"4","key":"576_CR39","first-page":"1","volume":"23","author":"ZR Yang","year":"2001","unstructured":"Yang ZR, Zwolinski M (2001) A mutual information theory for adaptive mixture models. IEEE Trans Pattern Anal Mach Intell 23(4):1\u20138","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"9","key":"576_CR40","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1109\/TPAMI.2004.71","volume":"26","author":"MHC Smyth","year":"2004","unstructured":"Smyth MHC, Figueiredo MAT, Jain AK (2004) Simultaneous feature selection and clustering using mixture models. IEEE Trans Pattern Anal Mach Intell 26(9):1154\u20131166","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"576_CR41","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.patcog.2005.09.012","volume":"39","author":"P Franti","year":"2006","unstructured":"Franti P, Virmajoki O (2006) Iterative shrinking method for clustering problems. Pattern Recognit 39(5):761\u2013765","journal-title":"Pattern Recognit"},{"key":"576_CR42","unstructured":"Frank A, Asuncion A (2010) UCI machine learning repository. \n                        http:\/\/archive.ics.uci.edu\/ml\n                        \n                    . University of California, School of Information and Computer Science, Irvine"},{"issue":"3","key":"576_CR43","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1109\/TNNLS.2012.2234134","volume":"24","author":"J Li","year":"2013","unstructured":"Li J, Tao D (2013) Simple exponential family PCA. IEEE Trans Neural Netw Learn Syst 24(3):485\u2013497","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"576_CR44","unstructured":"Rasmussen CE (1999) The infinite Gaussian mixture model. In: Advances in Neural Information Processing Systems 12, MIT Press, Cambridge, MA, pp 554\u2013560"},{"issue":"1","key":"576_CR45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jmp.2011.08.004","volume":"56","author":"SJ Gershman","year":"2012","unstructured":"Gershman SJ, Blei DM (2012) A tutorial on Bayesian nonparametric models. J Math Psychol 56(1):1\u201312","journal-title":"J Math Psychol"},{"key":"576_CR46","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1111\/1467-9868.00095","volume":"59","author":"S Richardson","year":"1997","unstructured":"Richardson S, Green PJ (1997) On Bayesian analysis of mixtures with an unknown number of components. J R Stat Soc B 59:731\u2013792","journal-title":"J R Stat Soc B"},{"key":"576_CR47","doi-asserted-by":"publisher","unstructured":"Song M, Wang H (2005) Highly efficient incremental estimation of Gaussian mixture models for online data stream clustering. In: Defense and security. Proc. SPIE 5803, Intelligent Computing: Theory and Applications III, pp 174\u2013183. doi:\n                        10.1117\/12.601724","DOI":"10.1117\/12.601724"},{"issue":"3","key":"576_CR48","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1109\/TNN.2007.891193","volume":"18","author":"J Lv","year":"2007","unstructured":"Lv J, Yi Z, Tan K (2007) Determination of the number of principal directions in a biologically plausible PCA model. IEEE Trans Neural Netw Learn Syst 18(3):910\u2013916","journal-title":"IEEE Trans Neural Netw Learn Syst"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10044-016-0576-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-016-0576-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-016-0576-5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-016-0576-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,2,15]],"date-time":"2018-02-15T07:10:54Z","timestamp":1518678654000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10044-016-0576-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,8,26]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,2]]}},"alternative-id":["576"],"URL":"https:\/\/doi.org\/10.1007\/s10044-016-0576-5","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"type":"print","value":"1433-7541"},{"type":"electronic","value":"1433-755X"}],"subject":[],"published":{"date-parts":[[2016,8,26]]}}}