{"id":"https://openalex.org/W2804389401","doi":"https://doi.org/10.1109/icassp.2018.8462323","title":"Self -Paced Mixture of T Distribution Model","display_name":"Self -Paced Mixture of T Distribution Model","publication_year":2018,"publication_date":"2018-04-01","ids":{"openalex":"https://openalex.org/W2804389401","doi":"https://doi.org/10.1109/icassp.2018.8462323","mag":"2804389401"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2018.8462323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8462323","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-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/A5100354603","display_name":"Yang Zhang","orcid":"https://orcid.org/0000-0001-8918-5280"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yang Zhang","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059534557","display_name":"Qingtao Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingtao Tang","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032618817","display_name":"Li Niu","orcid":"https://orcid.org/0000-0003-1970-8634"},"institutions":[{"id":"https://openalex.org/I74775410","display_name":"Rice University","ror":"https://ror.org/008zs3103","country_code":"US","type":"education","lineage":["https://openalex.org/I74775410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li Niu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Rice University, U.S"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Rice University, U.S","institution_ids":["https://openalex.org/I74775410"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023762528","display_name":"Tao Dai","orcid":"https://orcid.org/0000-0003-0594-6404"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Dai","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101600503","display_name":"Xi Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Xiao","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034104790","display_name":"Shu\u2010Tao Xia","orcid":"https://orcid.org/0000-0002-8639-982X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shu-Tao Xia","raw_affiliation_strings":["Department of Computer Science and Technology, Tsinghua University, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, Tsinghua University, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100354603"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.8461,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.80028828,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2796","last_page":"2800"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9991999864578247,"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":0.9991999864578247,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9742000102996826,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9617000222206116,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.9016887545585632},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.851436972618103},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.6645337343215942},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.619310736656189},{"id":"https://openalex.org/keywords/mixture-distribution","display_name":"Mixture distribution","score":0.5374098420143127},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4948522448539734},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4870765507221222},{"id":"https://openalex.org/keywords/laplace-distribution","display_name":"Laplace distribution","score":0.4765366017818451},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45566123723983765},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4442337155342102},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4412887692451477},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.43387675285339355},{"id":"https://openalex.org/keywords/students-t-distribution","display_name":"Student's t-distribution","score":0.41430768370628357},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3912349045276642},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.33800220489501953},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2977476418018341},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.2212713062763214},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.11224207282066345}],"concepts":[{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.9016887545585632},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.851436972618103},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.6645337343215942},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.619310736656189},{"id":"https://openalex.org/C56672385","wikidata":"https://www.wikidata.org/wiki/Q17157111","display_name":"Mixture distribution","level":3,"score":0.5374098420143127},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4948522448539734},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4870765507221222},{"id":"https://openalex.org/C183057437","wikidata":"https://www.wikidata.org/wiki/Q671617","display_name":"Laplace distribution","level":3,"score":0.4765366017818451},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45566123723983765},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4442337155342102},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4412887692451477},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.43387675285339355},{"id":"https://openalex.org/C49232408","wikidata":"https://www.wikidata.org/wiki/Q576072","display_name":"Student's t-distribution","level":4,"score":0.41430768370628357},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3912349045276642},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.33800220489501953},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2977476418018341},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2212713062763214},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.11224207282066345},{"id":"https://openalex.org/C55350006","wikidata":"https://www.wikidata.org/wiki/Q237193","display_name":"Exponential distribution","level":2,"score":0.0},{"id":"https://openalex.org/C23922673","wikidata":"https://www.wikidata.org/wiki/Q180752","display_name":"Autoregressive conditional heteroskedasticity","level":3,"score":0.0},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2018.8462323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8462323","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320331354","display_name":"Insight SFI Research Centre for Data Analytics","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W185904709","https://openalex.org/W306662213","https://openalex.org/W1938740620","https://openalex.org/W1990368529","https://openalex.org/W1992402718","https://openalex.org/W2018944012","https://openalex.org/W2051224630","https://openalex.org/W2110294373","https://openalex.org/W2113586398","https://openalex.org/W2118781519","https://openalex.org/W2132984949","https://openalex.org/W2137971377","https://openalex.org/W2160584648","https://openalex.org/W2358876993","https://openalex.org/W2532189199","https://openalex.org/W2574367259","https://openalex.org/W2740656851","https://openalex.org/W2740772560","https://openalex.org/W2741142724","https://openalex.org/W2802533528","https://openalex.org/W3016054986","https://openalex.org/W3120740533","https://openalex.org/W6607623680","https://openalex.org/W6648237178","https://openalex.org/W6676431381","https://openalex.org/W6676928000","https://openalex.org/W6679390333","https://openalex.org/W6731694940","https://openalex.org/W6741926674"],"related_works":["https://openalex.org/W2198732287","https://openalex.org/W4287816759","https://openalex.org/W2803473765","https://openalex.org/W2372918136","https://openalex.org/W2153481672","https://openalex.org/W2105786884","https://openalex.org/W2128540811","https://openalex.org/W4312864369","https://openalex.org/W1658960529","https://openalex.org/W4293056332"],"abstract_inverted_index":{"Gaussian":[0,23],"mixture":[1,24,47,50,92],"model":[2,8,25,104,133],"(GMM)":[3],"is":[4,118],"a":[5,34,87],"powerful":[6],"probabilistic":[7],"for":[9],"representing":[10],"the":[11,17,20,31,64,69,74,79,122,126,132,140,150],"probability":[12],"distribution":[13,103],"of":[14,22,37,44,48,51,59,66,81,93,101,128],"observations":[15],"in":[16],"population.":[18],"However,":[19],"fitness":[21],"can":[26,61],"be":[27],"significantly":[28],"degraded":[29],"when":[30],"data":[32,151],"contain":[33],"certain":[35,42],"amount":[36],"outliers.":[38,123,153],"Although":[39],"there":[40],"are":[41,71],"variants":[43],"GMM":[45],"(e.g.,":[46],"Laplace,":[49],"t":[52,94,102],"distribution)":[53],"attempting":[54],"to":[55,77,98,112,120,134],"handle":[56],"outliers,":[57],"none":[58],"them":[60],"sufficiently":[62],"mitigate":[63],"effect":[65,80],"outliers":[67,70,82],"if":[68],"far":[72],"from":[73],"centroids.":[75],"Aiming":[76],"remove":[78],"further,":[83],"this":[84],"paper":[85],"introduces":[86],"Self-Paced":[88,99],"Learning":[89],"mechanism":[90],"into":[91],"distribution,":[95],"which":[96],"leads":[97],"Mixture":[100],"(SPTMM).":[105],"We":[106],"derive":[107],"an":[108],"Expectation-Maximization":[109],"based":[110],"algorithm":[111],"train":[113],"SPTMM":[114,117,144],"and":[115,137],"show":[116],"able":[119],"screen":[121],"To":[124],"demonstrate":[125],"effectiveness":[127],"SPTMM,":[129],"we":[130],"apply":[131],"density":[135],"estimation":[136],"clustering.":[138],"Finally,":[139],"results":[141],"indicate":[142],"that":[143],"outperforms":[145],"other":[146],"methods,":[147],"especially":[148],"on":[149],"with":[152]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}
