{"id":"https://openalex.org/W4213268997","doi":"https://doi.org/10.1080/00207543.2022.2027040","title":"Deep embedding kernel mixture networks for conditional anomaly detection in high-dimensional data","display_name":"Deep embedding kernel mixture networks for conditional anomaly detection in high-dimensional data","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4213268997","doi":"https://doi.org/10.1080/00207543.2022.2027040"},"language":"en","primary_location":{"id":"doi:10.1080/00207543.2022.2027040","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00207543.2022.2027040","pdf_url":null,"source":{"id":"https://openalex.org/S65690446","display_name":"International Journal of Production Research","issn_l":"0020-7543","issn":["0020-7543","1366-588X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Production Research","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/A5085903475","display_name":"Hyojoong Kim","orcid":"https://orcid.org/0000-0002-1706-2991"},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyojoong Kim","raw_affiliation_strings":["Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST)","Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST)","institution_ids":["https://openalex.org/I157485424"]},{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hyojoong Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hyojoong Kim","raw_affiliation_strings":["Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST)","Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST)","institution_ids":["https://openalex.org/I157485424"]},{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Heeyoung Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Heeyoung Kim","raw_affiliation_strings":["Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST)","institution_ids":["https://openalex.org/I157485424"]}]},{"author_position":"last","author":{"id":null,"display_name":"Heeyoung Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Heeyoung Kim","raw_affiliation_strings":["Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST)","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157485424"],"apc_list":null,"apc_paid":null,"fwci":0.9279,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.77339557,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"61","issue":"4","first_page":"1101","last_page":"1113"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.996399998664856,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/embedding","display_name":"Embedding","score":0.7460713982582092},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.7036141157150269},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6652432084083557},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.6116207838058472},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5594275593757629},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.5226913690567017},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5184911489486694},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5116367936134338},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.48358434438705444},{"id":"https://openalex.org/keywords/kernel-regression","display_name":"Kernel regression","score":0.4791557192802429},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.473926842212677},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.4237472414970398},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39171290397644043},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3909373879432678},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2764808237552643},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1634087860584259},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.14838743209838867},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.06740966439247131}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.7460713982582092},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7036141157150269},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6652432084083557},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.6116207838058472},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5594275593757629},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.5226913690567017},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5184911489486694},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5116367936134338},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.48358434438705444},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.4791557192802429},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.473926842212677},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.4237472414970398},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39171290397644043},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3909373879432678},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2764808237552643},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1634087860584259},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.14838743209838867},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.06740966439247131},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/00207543.2022.2027040","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00207543.2022.2027040","pdf_url":null,"source":{"id":"https://openalex.org/S65690446","display_name":"International Journal of Production Research","issn_l":"0020-7543","issn":["0020-7543","1366-588X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Production Research","raw_type":"journal-article"},{"id":"pmh:oai:RePEc:taf:tprsxx:v:61:y:2023:i:4:p:1101-1113","is_oa":false,"landing_page_url":"http://hdl.handle.net/10.1080/00207543.2022.2027040","pdf_url":null,"source":{"id":"https://openalex.org/S4306401271","display_name":"RePEc: Research Papers in Economics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I77793887","host_organization_name":"Federal Reserve Bank of St. Louis","host_organization_lineage":["https://openalex.org/I77793887"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5211612291","display_name":null,"funder_award_id":"2018R1C1B6004511","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G5579799771","display_name":null,"funder_award_id":"2020R1A4A10187747","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W324141426","https://openalex.org/W1600438614","https://openalex.org/W1601795611","https://openalex.org/W2090507524","https://openalex.org/W2103914106","https://openalex.org/W2134255060","https://openalex.org/W2140095548","https://openalex.org/W2296719434","https://openalex.org/W2296761881","https://openalex.org/W2396781721","https://openalex.org/W2467760016","https://openalex.org/W2474015102","https://openalex.org/W2618706938","https://openalex.org/W2739793325","https://openalex.org/W2766452859","https://openalex.org/W2786088545","https://openalex.org/W2802761093","https://openalex.org/W2809701180","https://openalex.org/W2911727487","https://openalex.org/W2963748489","https://openalex.org/W2973694455","https://openalex.org/W2974193074","https://openalex.org/W2982719286","https://openalex.org/W2997977982","https://openalex.org/W3008558091","https://openalex.org/W3011249019","https://openalex.org/W3013163434","https://openalex.org/W3037587765","https://openalex.org/W3089513243","https://openalex.org/W3214294875","https://openalex.org/W6631190155","https://openalex.org/W6640963894","https://openalex.org/W6676014314","https://openalex.org/W6685488477"],"related_works":["https://openalex.org/W2406935090","https://openalex.org/W4296626987","https://openalex.org/W122742822","https://openalex.org/W2944714786","https://openalex.org/W171311476","https://openalex.org/W1594974787","https://openalex.org/W4213162954","https://openalex.org/W1995620735","https://openalex.org/W4241010850","https://openalex.org/W1507211460"],"abstract_inverted_index":{"In":[0,52,70],"various":[1],"industrial":[2],"problems,":[3],"sensor":[4],"data":[5,18,156,178],"are":[6,19,26,149,157],"often":[7],"used":[8,62],"to":[9,29,63,163],"detect":[10],"the":[11,30,45,65,112,117,120,138,154,171,181],"abnormal":[12],"state":[13],"of":[14,67,119,134,142,170],"manufacturing":[15],"systems.":[16],"Sensor":[17],"sometimes":[20],"influenced":[21],"by":[22],"contextual":[23,68,125],"variables":[24],"that":[25,153],"not":[27],"related":[28],"system":[31,46],"health":[32],"status":[33],"and":[34,83,98,111,184],"may":[35],"exhibit":[36],"different":[37],"behaviours":[38],"depending":[39],"on":[40,124],"their":[41],"values,":[42],"even":[43],"if":[44],"is":[47,174],"in":[48],"a":[49,55,75,87,131,160,185,189],"normal":[50],"condition.":[51],"this":[53,71],"case,":[54],"conditional":[56,76,123,135,165],"anomaly":[57,77],"detection":[58,78],"method":[59,95],"should":[60],"be":[61],"consider":[64],"effects":[66],"variables.":[69,126],"study,":[72],"we":[73],"propose":[74],"method,":[79],"particularly":[80],"for":[81],"high-dimensional":[82,109,155],"complex":[84],"data,":[85,110],"using":[86,137,176],"deep":[88,143],"embedding":[89,97,103],"kernel":[90,99,113],"mixture":[91,100,114],"network.":[92],"The":[93,102,127,146,168],"proposed":[94,172],"comprises":[96],"networks.":[101,145],"network":[104,115],"learns":[105],"low-dimensional":[106,161],"embeddings":[107,122],"from":[108,180,188],"models":[116],"distribution":[118],"learned":[121],"two":[128,147],"networks":[129,148],"enable":[130],"flexible":[132],"estimation":[133],"density":[136,166],"high":[139],"expressive":[140],"power":[141],"neural":[144],"trained":[150],"simultaneously":[151],"such":[152],"embedded":[158],"into":[159],"space,":[162],"assist":[164],"estimation.":[167],"effectiveness":[169],"model":[173],"demonstrated":[175],"real":[177],"examples":[179],"UCI":[182],"repository":[183],"case":[186],"study":[187],"tire":[190],"company.":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
