{"id":"https://openalex.org/W2913934873","doi":"https://doi.org/10.1109/bigdata.2018.8622120","title":"A Unified Unsupervised Gaussian Mixture Variational Autoencoder for High Dimensional Outlier Detection","display_name":"A Unified Unsupervised Gaussian Mixture Variational Autoencoder for High Dimensional Outlier Detection","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2913934873","doi":"https://doi.org/10.1109/bigdata.2018.8622120","mag":"2913934873"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2018.8622120","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622120","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 Big Data (Big Data)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5083406034","display_name":"Weixian Liao","orcid":"https://orcid.org/0000-0003-1444-8925"},"institutions":[{"id":"https://openalex.org/I4322298","display_name":"Towson University","ror":"https://ror.org/044w7a341","country_code":"US","type":"education","lineage":["https://openalex.org/I4322298"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weixian Liao","raw_affiliation_strings":["Department of Computer and Information Sciences, Towson University, Towson, MD"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Information Sciences, Towson University, Towson, MD","institution_ids":["https://openalex.org/I4322298"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026759570","display_name":"Yifan Guo","orcid":"https://orcid.org/0000-0002-9700-5005"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yifan Guo","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100773787","display_name":"Xuhui Chen","orcid":"https://orcid.org/0009-0003-3444-1226"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xuhui Chen","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100455171","display_name":"Pan Li","orcid":"https://orcid.org/0000-0001-6522-2446"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pan Li","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH","institution_ids":["https://openalex.org/I58956616"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4714,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.87536377,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1208","last_page":"1217"},"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/T10220","display_name":"Machine Fault Diagnosis Techniques","score":0.9779999852180481,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9623000025749207,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8810269832611084},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6991444826126099},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6147454977035522},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.605862557888031},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6017394065856934},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5701326727867126},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.558562159538269},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5034191012382507},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.46674519777297974},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.42460909485816956},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34096547961235046},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2811436057090759}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8810269832611084},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6991444826126099},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6147454977035522},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.605862557888031},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6017394065856934},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5701326727867126},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.558562159538269},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5034191012382507},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.46674519777297974},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.42460909485816956},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34096547961235046},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2811436057090759},{"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:10.1109/bigdata.2018.8622120","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2018.8622120","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 Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W111228695","https://openalex.org/W122039765","https://openalex.org/W1546503963","https://openalex.org/W1959608418","https://openalex.org/W1986332411","https://openalex.org/W1991357106","https://openalex.org/W2018588330","https://openalex.org/W2046660258","https://openalex.org/W2049633694","https://openalex.org/W2089554624","https://openalex.org/W2104052971","https://openalex.org/W2104837959","https://openalex.org/W2107789863","https://openalex.org/W2118871014","https://openalex.org/W2122646361","https://openalex.org/W2127979711","https://openalex.org/W2185125590","https://openalex.org/W2282861635","https://openalex.org/W2290128307","https://openalex.org/W2302958273","https://openalex.org/W2346714907","https://openalex.org/W2398119937","https://openalex.org/W2464234964","https://openalex.org/W2554148185","https://openalex.org/W2556467266","https://openalex.org/W2606068338","https://openalex.org/W2743138268","https://openalex.org/W2780823890","https://openalex.org/W2783452787","https://openalex.org/W2786088545","https://openalex.org/W2801442861","https://openalex.org/W2807955733","https://openalex.org/W2913556461","https://openalex.org/W2962695963","https://openalex.org/W2963166639","https://openalex.org/W3153872861","https://openalex.org/W4299345493","https://openalex.org/W6605102760","https://openalex.org/W6640963894","https://openalex.org/W6676071220","https://openalex.org/W6677585907","https://openalex.org/W6686152136","https://openalex.org/W6712574313","https://openalex.org/W6719357382","https://openalex.org/W6730018140","https://openalex.org/W6730333270","https://openalex.org/W6748102297","https://openalex.org/W6758841785","https://openalex.org/W7016021835","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W3186512740","https://openalex.org/W3017266184","https://openalex.org/W3194885736","https://openalex.org/W4310873165","https://openalex.org/W3046391934","https://openalex.org/W4363671829","https://openalex.org/W2355395139","https://openalex.org/W1992295166","https://openalex.org/W2143508933"],"abstract_inverted_index":{"Paradigm-shifting":[0],"systems":[1,20],"such":[2,27],"as":[3,28,66],"cyber-physical":[4],"systems,":[5],"collect":[6],"data":[7],"of":[8,19,46,81],"high-":[9],"or":[10],"ultrahigh-":[11],"dimensionality":[12,36],"tremendously.":[13],"Detecting":[14],"outliers":[15,188],"in":[16,24,52,75,90,97,221],"this":[17,98],"type":[18],"provides":[21],"indicative":[22],"understanding":[23],"wide-ranging":[25],"domains":[26],"system":[29],"health":[30],"monitoring,":[31],"information":[32,51],"security,":[33],"etc.":[34],"Previous":[35],"reduction":[37],"based":[38,124],"outlier":[39,110,212],"detection":[40,213],"methods":[41],"suffer":[42],"from":[43],"the":[44,49,53,72,82,85,135,140,151,160,171,174,179,184,190,207],"incapability":[45],"well":[47],"preserving":[48],"critical":[50],"low-dimensional":[54],"latent":[55,141],"space,":[56],"mainly":[57],"because":[58],"they":[59],"generally":[60],"assume":[61],"an":[62],"isotropic":[63],"Gaussian":[64,105,152,180],"distribution":[65,120,142],"prior":[67],"and":[68,121,143,178,215],"fail":[69],"to":[70,133,155],"mine":[71],"intrinsic":[73],"multimodality":[74],"high":[76],"dimensional":[77],"data.":[78],"Moreover,":[79],"most":[80],"schemes":[83,214],"decouple":[84],"model":[86,154,166],"learning":[87],"process,":[88],"resulting":[89],"suboptimal":[91],"performance.":[92],"To":[93],"tackle":[94],"these":[95],"challenges,":[96],"paper,":[99],"we":[100,127],"propose":[101],"a":[102,113,118,129,195],"unified":[103],"Unsupervised":[104],"Mixture":[106],"Variational":[107],"Autoencoder":[108],"for":[109],"detection.":[111],"Specifically,":[112],"variational":[114,172],"autoencoder":[115],"firstly":[116],"trains":[117],"generative":[119],"extracts":[122],"reconstruction":[123],"features.":[125],"Then":[126],"adopt":[128],"deep":[130,175],"brief":[131,176],"network":[132],"estimate":[134,156],"component":[136],"mixture":[137,153,181],"probabilities":[138],"by":[139,150],"extracted":[144],"features,":[145],"which":[146],"is":[147,167],"further":[148],"used":[149],"sample":[157,192],"densities":[158],"with":[159,170],"Expectation-Maximization":[161],"(EM)":[162],"algorithm.":[163],"The":[164],"inference":[165],"optimized":[168],"jointly":[169],"autoencoder,":[173],"network,":[177],"model.":[182],"Afterwards,":[183],"proposed":[185,208],"detector":[186],"identifies":[187],"when":[189],"estimated":[191],"density":[193],"exceeds":[194],"learned":[196],"threshold.":[197],"Extensive":[198],"simulations":[199],"on":[200,217],"six":[201],"public":[202],"benchmark":[203],"datasets":[204],"show":[205],"that":[206],"framework":[209],"outperforms":[210],"state-of-the-art":[211],"achieves,":[216],"average,":[218],"27%":[219],"improvements":[220],"F1":[222],"score.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":6}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
