{"id":"https://openalex.org/W3036890664","doi":"https://doi.org/10.1109/tbdata.2020.3004057","title":"Fast Multi-View Outlier Detection via Deep Encoder","display_name":"Fast Multi-View Outlier Detection via Deep Encoder","publication_year":2020,"publication_date":"2020-06-22","ids":{"openalex":"https://openalex.org/W3036890664","doi":"https://doi.org/10.1109/tbdata.2020.3004057","mag":"3036890664"},"language":"en","primary_location":{"id":"doi:10.1109/tbdata.2020.3004057","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2020.3004057","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Big Data","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/A5060691225","display_name":"Dongdong Hou","orcid":"https://orcid.org/0000-0002-4400-4295"},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dongdong Hou","raw_affiliation_strings":["State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, China","University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006477225","display_name":"Yang Cong","orcid":"https://orcid.org/0000-0002-5102-0189"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Cong","raw_affiliation_strings":["State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031333887","display_name":"Gan Sun","orcid":"https://orcid.org/0000-0003-1111-6909"},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gan Sun","raw_affiliation_strings":["State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014443478","display_name":"Jiahua Dong","orcid":"https://orcid.org/0000-0001-8545-4447"},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahua Dong","raw_affiliation_strings":["State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, China","University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Robotics, Shenyang Institute of Automation, Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning, China","institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100361625","display_name":"Jun Li","orcid":"https://orcid.org/0000-0001-5845-8602"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Li","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100399824","display_name":"Kai Li","orcid":"https://orcid.org/0000-0001-8452-620X"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kai Li","raw_affiliation_strings":["Department of Electrical and Computer Engineering, College of Engineering, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, College of Engineering, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5060691225"],"corresponding_institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366","https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.5302,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.72269868,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"8","issue":"4","first_page":"1047","last_page":"1058"},"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/T11220","display_name":"Water Systems and Optimization","score":0.9628999829292297,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9628999829292297,"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/computer-science","display_name":"Computer science","score":0.8329445123672485},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7807497978210449},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7268025875091553},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6698967218399048},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6520410776138306},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6282187700271606},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5830624103546143},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5711292624473572},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5481685400009155},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4417873024940491},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4173264503479004},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3674495816230774},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34467369318008423}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8329445123672485},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7807497978210449},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7268025875091553},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6698967218399048},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6520410776138306},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6282187700271606},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5830624103546143},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5711292624473572},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5481685400009155},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4417873024940491},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4173264503479004},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3674495816230774},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34467369318008423},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tbdata.2020.3004057","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tbdata.2020.3004057","pdf_url":null,"source":{"id":"https://openalex.org/S2491400915","display_name":"IEEE Transactions on Big Data","issn_l":"2332-7790","issn":["2332-7790","2372-2096"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Big Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1460833335","display_name":null,"funder_award_id":"61533015","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1600926656","display_name":null,"funder_award_id":"61722311","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2389678321","display_name":null,"funder_award_id":"U1613214","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3507080720","display_name":null,"funder_award_id":"61821005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W4333350","https://openalex.org/W297461772","https://openalex.org/W580709845","https://openalex.org/W1201875361","https://openalex.org/W1546847949","https://openalex.org/W1982893028","https://openalex.org/W1986332411","https://openalex.org/W1997201895","https://openalex.org/W2012931101","https://openalex.org/W2019885956","https://openalex.org/W2035128422","https://openalex.org/W2048030021","https://openalex.org/W2072072671","https://openalex.org/W2083177649","https://openalex.org/W2090101874","https://openalex.org/W2103972604","https://openalex.org/W2110784166","https://openalex.org/W2113573459","https://openalex.org/W2137130182","https://openalex.org/W2147800946","https://openalex.org/W2156686129","https://openalex.org/W2167332923","https://openalex.org/W2169529055","https://openalex.org/W2295576075","https://openalex.org/W2405459681","https://openalex.org/W2518937691","https://openalex.org/W2736191430","https://openalex.org/W2741998188","https://openalex.org/W2752197020","https://openalex.org/W2758021822","https://openalex.org/W2776638780","https://openalex.org/W2780100774","https://openalex.org/W2788954130","https://openalex.org/W2793890232","https://openalex.org/W2797647736","https://openalex.org/W2814803617","https://openalex.org/W2894947093","https://openalex.org/W2902343868","https://openalex.org/W2906715444","https://openalex.org/W2907897631","https://openalex.org/W2962911132","https://openalex.org/W2966840649","https://openalex.org/W2989440555","https://openalex.org/W2990654629","https://openalex.org/W2998507417","https://openalex.org/W3011226933","https://openalex.org/W3035260401","https://openalex.org/W3112808477","https://openalex.org/W4296746587","https://openalex.org/W6603183647","https://openalex.org/W6632551490","https://openalex.org/W6675134712","https://openalex.org/W6682991666","https://openalex.org/W6697109910"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W3186512740","https://openalex.org/W2499612753","https://openalex.org/W4363671829","https://openalex.org/W2780476542","https://openalex.org/W3111802945","https://openalex.org/W2946096271","https://openalex.org/W2295423552","https://openalex.org/W1598471830","https://openalex.org/W3107369729"],"abstract_inverted_index":{"Multi-view":[0],"outlier":[1,26,59,70],"detection":[2,27,71],"has":[3,10],"a":[4,67,75,103,116,125,148,218],"wide":[5],"range":[6],"of":[7,57,118],"applications":[8],"and":[9,145,150,160,217,232],"been":[11],"well":[12],"investigated":[13],"in":[14,200],"recent":[15],"years.":[16],"However,":[17],"1)":[18],"most":[19],"existing":[20,228],"state-of-the-art":[21],"methods":[22],"cannot":[23],"efficiently":[24,88,183],"handle":[25],"problem":[28],"for":[29,92,188],"large-scale":[30,93,201],"multi-view":[31,58,69],"data,":[32],"since":[33],"exploring":[34],"pairwise":[35],"constraints":[36],"among":[37],"different":[38],"views":[39,206],"causes":[40],"highly-computational":[41],"cost;":[42],"2)":[43],"the":[44,54,90,111,133,140,158,168,177,185,189,198,227],"data":[45,94,98,142,205],"collected":[46],"from":[47,124,132,137,203],"original":[48,134],"heterogeneous":[49],"feature":[50],"spaces":[51],"further":[52],"increases":[53],"consistent":[55],"difficulty":[56],"detection.":[60],"To":[61],"address":[62],"these":[63],"issues,":[64],"we":[65,114],"present":[66],"fast":[68],"model":[72,194,225],"via":[73],"learning":[74],"low-rank":[76],"latent":[77,106,152,171],"subspace":[78,107,153],"representation":[79],"with":[80,96,207],"deep":[81,179],"encoder":[82],"architecture,":[83],"which":[84,128,155,181],"can":[85,182,195],"not":[86],"only":[87],"identify":[89,197],"outliers":[91,199],"even":[95],"numerous":[97,204],"views,":[99,165],"but":[100],"also":[101],"exploit":[102],"discriminative":[104,151,170],"common":[105],"shared":[108,149],"by":[109],"all":[110],"views.":[112],"First,":[113],"learn":[115],"set":[117],"orthogonal":[119],"bases":[120],"as":[121,147],"view-specific":[122,138,161,178],"dictionaries":[123],"small":[126],"dataset,":[127],"is":[129,143],"randomly":[130],"sampled":[131,141],"dataset.":[135],"Benefitting":[136],"dictionaries,":[139],"projected":[144],"decomposed":[146],"representations,":[154],"correspond":[156],"to":[157,175],"view-consistent":[159],"components":[162],"across":[163],"multiple":[164],"respectively.":[166],"Then,":[167],"obtained":[169],"representations":[172],"are":[173],"applied":[174],"train":[176],"encoders,":[180],"compute":[184],"abnormal":[186],"score":[187],"remaining":[190],"instances.":[191],"Our":[192],"proposed":[193,224],"cost-effectively":[196],"datasets":[202,216],"less":[208],"computational":[209],"complexity.":[210],"Experiments":[211],"conducted":[212],"on":[213,230],"eight":[214],"real":[215],"synthesis":[219],"dataset":[220],"show":[221],"that":[222],"our":[223],"outperforms":[226],"ones":[229],"effectiveness":[231],"efficiency.":[233]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}