{"id":"https://openalex.org/W4416017626","doi":"https://doi.org/10.1145/3746252.3761354","title":"Fast Outlier Detection in Oblique Subspaces","display_name":"Fast Outlier Detection in Oblique Subspaces","publication_year":2025,"publication_date":"2025-11-08","ids":{"openalex":"https://openalex.org/W4416017626","doi":"https://doi.org/10.1145/3746252.3761354"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3761354","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761354","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","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":null,"display_name":"Bowen Li","orcid":"https://orcid.org/0009-0003-8928-5195"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bowen Li","raw_affiliation_strings":["Florida State University, Tallahassee, FL, USA"],"raw_orcid":"https://orcid.org/0009-0003-8928-5195","affiliations":[{"raw_affiliation_string":"Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028089542","display_name":"Char\u0173 C. Aggarwal","orcid":"https://orcid.org/0000-0003-2579-7581"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Charu C. Aggarwal","raw_affiliation_strings":["IBM T. J. Watson Research Center, Yorktown Heights, NY, USA"],"raw_orcid":"https://orcid.org/0000-0003-2579-7581","affiliations":[{"raw_affiliation_string":"IBM T. J. Watson Research Center, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103834032","display_name":"Peixiang Zhao","orcid":"https://orcid.org/0000-0002-8848-5852"},"institutions":[{"id":"https://openalex.org/I103163165","display_name":"Florida State University","ror":"https://ror.org/05g3dte14","country_code":"US","type":"education","lineage":["https://openalex.org/I103163165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Peixiang Zhao","raw_affiliation_strings":["Florida State University, Tallahassee, FL, USA"],"raw_orcid":"https://orcid.org/0000-0002-8848-5852","affiliations":[{"raw_affiliation_string":"Florida State University, Tallahassee, FL, USA","institution_ids":["https://openalex.org/I103163165"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1530","last_page":"1539"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9488000273704529,"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":0.9488000273704529,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.00800000037997961,"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"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.00419999985024333,"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/linear-subspace","display_name":"Linear subspace","score":0.8169999718666077},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7157999873161316},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6714000105857849},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.6259999871253967},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5914000272750854},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.5443000197410583},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.5408999919891357},{"id":"https://openalex.org/keywords/oblique-case","display_name":"Oblique case","score":0.49630001187324524},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.4950000047683716}],"concepts":[{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.8169999718666077},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7157999873161316},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6714000105857849},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.6259999871253967},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6111999750137329},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5914000272750854},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.5443000197410583},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.5408999919891357},{"id":"https://openalex.org/C160697094","wikidata":"https://www.wikidata.org/wiki/Q1233197","display_name":"Oblique case","level":2,"score":0.49630001187324524},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.4950000047683716},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4918999969959259},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45980000495910645},{"id":"https://openalex.org/C187062812","wikidata":"https://www.wikidata.org/wiki/Q6322840","display_name":"K-independent hashing","level":5,"score":0.44920000433921814},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4223000109195709},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4009999930858612},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.38040000200271606},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3343999981880188},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3285999894142151},{"id":"https://openalex.org/C161666082","wikidata":"https://www.wikidata.org/wiki/Q14551923","display_name":"Oblique projection","level":3,"score":0.30390000343322754},{"id":"https://openalex.org/C201932085","wikidata":"https://www.wikidata.org/wiki/Q642514","display_name":"Online analytical processing","level":3,"score":0.3009999990463257},{"id":"https://openalex.org/C87431388","wikidata":"https://www.wikidata.org/wiki/Q2070573","display_name":"Perfect hash function","level":4,"score":0.30059999227523804},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.27730000019073486},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C156340839","wikidata":"https://www.wikidata.org/wiki/Q2704791","display_name":"Enumeration","level":2,"score":0.2619999945163727},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.2612999975681305},{"id":"https://openalex.org/C16038011","wikidata":"https://www.wikidata.org/wiki/Q62912","display_name":"Cartesian coordinate system","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.25529998540878296}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3761354","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761354","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","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":28,"referenced_works":["https://openalex.org/W1986332411","https://openalex.org/W1995443851","https://openalex.org/W2000661457","https://openalex.org/W2002075846","https://openalex.org/W2041184937","https://openalex.org/W2056081083","https://openalex.org/W2061122559","https://openalex.org/W2080234606","https://openalex.org/W2097017789","https://openalex.org/W2097714558","https://openalex.org/W2101549186","https://openalex.org/W2110784166","https://openalex.org/W2116300222","https://openalex.org/W2124536999","https://openalex.org/W2142498761","https://openalex.org/W2144182447","https://openalex.org/W2170902455","https://openalex.org/W2396867696","https://openalex.org/W2516208336","https://openalex.org/W2516815969","https://openalex.org/W2783928289","https://openalex.org/W2809244162","https://openalex.org/W2884123408","https://openalex.org/W2904176920","https://openalex.org/W2963606058","https://openalex.org/W3107582219","https://openalex.org/W3125917269","https://openalex.org/W4253461361"],"related_works":[],"abstract_inverted_index":{"Subspace":[0,113],"outlier":[1,106,142,165],"detection":[2,166],"is":[3,115],"a":[4,66,116],"fundamental":[5],"data":[6,11,48,89,137,154],"mining":[7],"task":[8],"for":[9,102,140],"high-dimensional":[10],"with":[12],"diverse":[13],"applications":[14],"across":[15],"widely":[16],"varying":[17],"domains.":[18],"Existing":[19],"subspace":[20,99,141],"hashing":[21,88,100],"methods":[22,40,167],"have":[23],"been":[24],"capable":[25],"of":[26,69,83,160,170],"identifying":[27],"accurate":[28],"and":[29,59,104,129,152,158,174],"interpretable":[30],"outliers":[31],"in":[32,35,168],"axis-parallel":[33],"subspaces":[34,71],"linear":[36],"time.":[37],"However,":[38],"these":[39],"simply":[41],"fail":[42],"when":[43],"applied":[44],"to":[45,121,135],"arbitrary-shaped,":[46],"schema-less":[47],"that":[49],"lack":[50],"well-defined":[51],"attributes":[52],"or":[53],"dimensions,":[54],"such":[55],"as":[56],"time":[57,150],"series":[58],"graphs.":[60],"In":[61],"this":[62],"paper,":[63],"we":[64,96],"introduce":[65],"new":[67],"notion":[68],"oblique":[70,94],"defined":[72],"on":[73,147],"pairwise":[74],"object":[75],"proximity":[76],"functions":[77],"without":[78],"requiring":[79],"explicit":[80],"multidimensional":[81,124],"representations":[82],"the":[84,136,156],"underlying":[85],"data.":[86],"By":[87],"objects":[90],"into":[91],"arbitrarily":[92],"oriented":[93],"subspaces,":[95],"can":[97,131],"construct":[98],"histograms":[101],"efficient":[103],"cost-effective":[105],"detection.":[107,143],"Our":[108,144],"proposed":[109],"solution,":[110],"OS-Hash":[111],"(Oblique":[112],"Hashing),":[114],"linear-time,":[117],"constant-space":[118],"method":[119],"applicable":[120],"not":[122],"only":[123],"but":[125],"also":[126],"arbitrary-shaped":[127],"data,":[128],"it":[130],"further":[132],"be":[133],"extended":[134],"stream":[138],"setting":[139],"experimental":[145],"studies":[146],"real-world":[148],"multidimensional,":[149],"series,":[151],"graph":[153],"demonstrate":[155],"efficiency":[157],"efficacy":[159],"OS-Hash,":[161],"which":[162],"outperforms":[163],"state-of-the-art":[164],"terms":[169],"both":[171],"runtime":[172],"performance":[173],"accuracy.":[175]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-08T00:00:00"}