{"id":"https://openalex.org/W1991136330","doi":"https://doi.org/10.1109/cvpr.2011.5995485","title":"Max-margin clustering: Detecting margins from projections of points on lines","display_name":"Max-margin clustering: Detecting margins from projections of points on lines","publication_year":2011,"publication_date":"2011-06-01","ids":{"openalex":"https://openalex.org/W1991136330","doi":"https://doi.org/10.1109/cvpr.2011.5995485","mag":"1991136330"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2011.5995485","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2011.5995485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CVPR 2011","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/A5036016228","display_name":"Raghuraman Gopalan","orcid":null},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raghuraman Gopalan","raw_affiliation_strings":["Center of Automation Research, University of Maryland, USA","Center for Automation Research, University of Maryland#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center of Automation Research, University of Maryland, USA","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"Center for Automation Research, University of Maryland#TAB#","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050157577","display_name":"Jagan Sankaranarayanan","orcid":"https://orcid.org/0009-0006-0369-816X"},"institutions":[{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jagan Sankaranarayanan","raw_affiliation_strings":["Center of Automation Research, University of Maryland, USA","Center for Automation Research, University of Maryland#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center of Automation Research, University of Maryland, USA","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"Center for Automation Research, University of Maryland#TAB#","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"5","issue":null,"first_page":"2769","last_page":"2776"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9904000163078308,"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/cluster-analysis","display_name":"Cluster analysis","score":0.759006142616272},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6844123601913452},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.6742706298828125},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5547632575035095},{"id":"https://openalex.org/keywords/separable-space","display_name":"Separable space","score":0.536508321762085},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.535327136516571},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5257359743118286},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5225405097007751},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.49897265434265137},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.48367366194725037},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48222029209136963},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.42742520570755005},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.36787617206573486},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2012556791305542}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.759006142616272},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6844123601913452},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6742706298828125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5547632575035095},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.536508321762085},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.535327136516571},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5257359743118286},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5225405097007751},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49897265434265137},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.48367366194725037},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48222029209136963},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.42742520570755005},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.36787617206573486},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2012556791305542},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2011.5995485","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2011.5995485","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"CVPR 2011","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.362.8699","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.362.8699","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.umiacs.umd.edu/~raghuram/Publications/2011_CVPR_MaxMarginClustering.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320333591","display_name":"Multidisciplinary University Research Initiative","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":51,"referenced_works":["https://openalex.org/W1526146785","https://openalex.org/W1588401315","https://openalex.org/W1589666435","https://openalex.org/W1770825568","https://openalex.org/W1943383135","https://openalex.org/W1971784203","https://openalex.org/W1992419399","https://openalex.org/W2006793117","https://openalex.org/W2053186076","https://openalex.org/W2097308346","https://openalex.org/W2105120842","https://openalex.org/W2106404777","https://openalex.org/W2112796928","https://openalex.org/W2115245664","https://openalex.org/W2121947440","https://openalex.org/W2123921160","https://openalex.org/W2127218421","https://openalex.org/W2128487019","https://openalex.org/W2132771435","https://openalex.org/W2132820034","https://openalex.org/W2135346934","https://openalex.org/W2139212933","https://openalex.org/W2148214025","https://openalex.org/W2149667092","https://openalex.org/W2149817402","https://openalex.org/W2149982386","https://openalex.org/W2150102617","https://openalex.org/W2155759509","https://openalex.org/W2157791002","https://openalex.org/W2162630660","https://openalex.org/W2165874743","https://openalex.org/W2168466186","https://openalex.org/W2171692249","https://openalex.org/W2319660501","https://openalex.org/W2799061466","https://openalex.org/W3004533406","https://openalex.org/W3120740533","https://openalex.org/W4241675681","https://openalex.org/W4244494905","https://openalex.org/W4285719527","https://openalex.org/W6631556622","https://openalex.org/W6635204224","https://openalex.org/W6635310694","https://openalex.org/W6678914141","https://openalex.org/W6679435521","https://openalex.org/W6679854563","https://openalex.org/W6681759745","https://openalex.org/W6681875376","https://openalex.org/W6682274981","https://openalex.org/W6682953061","https://openalex.org/W6684578312"],"related_works":["https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2370917603","https://openalex.org/W2009525028","https://openalex.org/W2952760143","https://openalex.org/W2017776670","https://openalex.org/W4321064619","https://openalex.org/W3006513224","https://openalex.org/W4318818647","https://openalex.org/W2944968625"],"abstract_inverted_index":{"Given":[0],"a":[1,18,47,53,99,118],"unlabelled":[2],"set":[3,71],"of":[4,67,72,103,106,110,164,178],"points":[5,41,58],"X":[6,68],"\u03f5":[7],"\u211d":[8,78],"<sup":[9,79],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[10,80],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">N</sup>":[11,81],"belonging":[12,59],"to":[13,20,43,56,60,122,155,186],"k":[14],"groups,":[15],"we":[16,83,127],"propose":[17],"method":[19,139],"identify":[21],"cluster":[22,124],"assignments":[23],"that":[24,89],"provides":[25],"maximum":[26],"separating":[27],"margin":[28,44],"among":[29],"the":[30,65,70,133,161,182],"clusters.":[31],"We":[32,112,136],"address":[33],"this":[34],"problem":[35],"by":[36,93],"exploiting":[37],"sparsity":[38],"in":[39,77,158,175],"data":[40],"inherent":[42],"regions,":[45],"which":[46],"max-margin":[48],"classifier":[49],"would":[50],"produce":[51],"under":[52,101],"supervised":[54],"setting":[55],"separate":[57],"different":[61],"groups.":[62],"By":[63],"analyzing":[64],"projections":[66],"on":[69,140,145,181],"all":[73],"possible":[74],"lines":[75],"L":[76],",":[82],"first":[84],"establish":[85],"some":[86,146],"basic":[87],"results":[88,116],"are":[90],"satisfied":[91],"only":[92],"those":[94],"line":[95],"intervals":[96],"lying":[97],"outside":[98],"cluster,":[100],"assumptions":[102],"linear":[104],"separability":[105],"clusters":[107,131,165],"and":[108,144,150,157,184],"absence":[109],"outliers.":[111],"then":[113],"encode":[114],"these":[115],"into":[117],"pair-wise":[119],"similarity":[120],"measure":[121],"determine":[123],"assignments,":[125],"where":[126,160],"accommodate":[128],"non-linearly":[129],"separable":[130],"using":[132],"kernel":[134],"trick.":[135],"validate":[137],"our":[138],"several":[141,191],"UCI":[142],"datasets":[143],"computer":[147],"vision":[148],"problems,":[149],"empirically":[151],"show":[152],"its":[153],"robustness":[154],"outliers,":[156],"cases":[159],"exact":[162],"number":[163],"is":[166],"not":[167],"available.":[168],"The":[169],"proposed":[170],"approach":[171],"offers":[172],"an":[173],"improvement":[174],"clustering":[176],"accuracy":[177],"about":[179],"6%":[180],"average,":[183],"up":[185],"15%":[187],"when":[188],"compared":[189],"with":[190],"existing":[192],"methods.":[193]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
