{"id":"https://openalex.org/W1998035802","doi":"https://doi.org/10.1142/s0218001404003812","title":"ENTROPY PENALIZED AUTOMATED MODEL SELECTION ON GAUSSIAN MIXTURE","display_name":"ENTROPY PENALIZED AUTOMATED MODEL SELECTION ON GAUSSIAN MIXTURE","publication_year":2004,"publication_date":"2004-12-01","ids":{"openalex":"https://openalex.org/W1998035802","doi":"https://doi.org/10.1142/s0218001404003812","mag":"1998035802"},"language":"en","primary_location":{"id":"doi:10.1142/s0218001404003812","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001404003812","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067164993","display_name":"Jinwen Ma","orcid":"https://orcid.org/0000-0002-7388-4295"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"JINWEN MA","raw_affiliation_strings":["Department of Information Science, School of Mathematical Sciences & LMAM, Peking University, Beijing, 100871, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information Science, School of Mathematical Sciences & LMAM, Peking University, Beijing, 100871, P. R. China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055344991","display_name":"Taijun Wang","orcid":"https://orcid.org/0000-0003-2008-9121"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"TAIJUN WANG","raw_affiliation_strings":["Department of Wireless Engineering, Southeast University, Nanjing, 210018, P. R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Wireless Engineering, Southeast University, Nanjing, 210018, P. R. China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8504,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.87382552,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"18","issue":"08","first_page":"1501","last_page":"1512"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9713000059127808,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9713000059127808,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9641000032424927,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9628000259399414,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.8250594139099121},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.6771749258041382},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.6458066701889038},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5693532228469849},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5489252209663391},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5487360954284668},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.511078953742981},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.4942021667957306},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4939880967140198},{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.4791916608810425},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47618380188941956},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36605554819107056}],"concepts":[{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.8250594139099121},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.6771749258041382},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.6458066701889038},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5693532228469849},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5489252209663391},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5487360954284668},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.511078953742981},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.4942021667957306},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4939880967140198},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.4791916608810425},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47618380188941956},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36605554819107056},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218001404003812","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001404003812","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W116926728","https://openalex.org/W1490454746","https://openalex.org/W1569447425","https://openalex.org/W1587362683","https://openalex.org/W1974364887","https://openalex.org/W1981367467","https://openalex.org/W1981903823","https://openalex.org/W1992402718","https://openalex.org/W2021056606","https://openalex.org/W2045908891","https://openalex.org/W2071781501","https://openalex.org/W2073568237","https://openalex.org/W2116111483","https://openalex.org/W2126816458","https://openalex.org/W2142635246","https://openalex.org/W2149578361","https://openalex.org/W2168175751","https://openalex.org/W2168993425","https://openalex.org/W2170986055","https://openalex.org/W2171975443","https://openalex.org/W2325346015","https://openalex.org/W2536251287","https://openalex.org/W2545247393","https://openalex.org/W2977466517","https://openalex.org/W6921331664"],"related_works":["https://openalex.org/W1952261593","https://openalex.org/W2183453817","https://openalex.org/W2536221661","https://openalex.org/W3021672241","https://openalex.org/W1853799209","https://openalex.org/W2147926259","https://openalex.org/W1998035802","https://openalex.org/W2025931908","https://openalex.org/W3041490575","https://openalex.org/W2970690932"],"abstract_inverted_index":{"Gaussian":[0,25,48],"mixture":[1,26,49],"modeling":[2,50],"is":[3,20,58],"a":[4,39],"powerful":[5],"approach":[6],"for":[7,47],"data":[8],"analysis":[9],"and":[10,100],"the":[11,14,22,61,64,73,77,81,90,103],"determination":[12],"of":[13,16,24,42,80,105],"number":[15,104],"Gaussians,":[17],"or":[18],"clusters,":[19],"actually":[21],"problem":[23],"model":[27,44,69,93],"selection":[28,45,70,94],"which":[29],"has":[30],"been":[31],"investigated":[32],"from":[33],"several":[34],"respects.":[35],"This":[36],"paper":[37],"proposes":[38],"new":[40],"kind":[41],"automated":[43,92],"algorithm":[46,66],"via":[51],"an":[52],"entropy":[53],"penalized":[54],"maximum-likelihood":[55],"estimation.":[56],"It":[57],"demonstrated":[59],"by":[60],"experiments":[62],"that":[63],"proposed":[65],"can":[67],"make":[68],"automatically":[71],"during":[72],"parameter":[74],"estimation,":[75],"with":[76,89],"mixing":[78],"proportions":[79],"extra":[82],"Gaussians":[83],"attenuating":[84],"to":[85],"zero.":[86],"As":[87],"compared":[88],"BYY":[91],"algorithms,":[95],"it":[96],"converges":[97],"more":[98],"stably":[99],"accurately":[101],"as":[102],"samples":[106],"becomes":[107],"large.":[108]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}