{"id":"https://openalex.org/W2104037165","doi":"https://doi.org/10.1109/cvpr.2010.5539861","title":"Robust RVM regression using sparse outlier model","display_name":"Robust RVM regression using sparse outlier model","publication_year":2010,"publication_date":"2010-06-01","ids":{"openalex":"https://openalex.org/W2104037165","doi":"https://doi.org/10.1109/cvpr.2010.5539861","mag":"2104037165"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2010.5539861","doi":"https://doi.org/10.1109/cvpr.2010.5539861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5539861","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5006862080","display_name":"Kaushik Mitra","orcid":"https://orcid.org/0000-0001-6747-9050","observed_orcids":["https://orcid.org/0000-0001-6747-9050"]},"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":"Kaushik Mitra","raw_affiliation_strings":["Department of Electrical and Computer Engineering and Center for Automation Research, UMIACS, University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering and Center for Automation Research, UMIACS, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073971393","display_name":"Ashok Veeraraghavan","orcid":"https://orcid.org/0000-0001-5043-7460","observed_orcids":["https://orcid.org/0000-0001-5043-7460"]},"institutions":[{"id":"https://openalex.org/I4405253225","display_name":"Mitsubishi Electric Research Laboratories (United States)","ror":"https://ror.org/00e2e3n51","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4405253225"]},{"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":"Ashok Veeraraghavan","raw_affiliation_strings":["Department of Electrical and Computer Engineering and Center for Automation Research, UMIACS, University of Maryland, College Park, MD, USA","Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering and Center for Automation Research, UMIACS, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA, USA","institution_ids":["https://openalex.org/I4405253225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102762707","display_name":"Rama Chellappa","orcid":"https://orcid.org/0000-0002-7638-1650","observed_orcids":["https://orcid.org/0000-0002-7638-1650"]},"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":"Rama Chellappa","raw_affiliation_strings":["Department of Electrical and Computer Engineering and Center for Automation Research, UMIACS, University of Maryland, College Park, MD, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering and Center for Automation Research, UMIACS, University of Maryland, College Park, MD, USA","institution_ids":["https://openalex.org/I66946132"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0095,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.87796728,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1887","last_page":"1894"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.33980000019073486,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.33980000019073486,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.2808000147342682,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.13650000095367432,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/relevance-vector-machine","display_name":"relevance vector machine","score":0.8330000042915344},{"id":"https://openalex.org/keywords/robust-regression","display_name":"robust regression","score":0.9909999966621399},{"id":"https://openalex.org/keywords/bayesian-estimation","display_name":"Bayesian estimation","score":0.4569999873638153},{"id":"https://openalex.org/keywords/basis-pursuit-denoising","display_name":"basis pursuit denoising","score":0.9940000176429749},{"id":"https://openalex.org/keywords/image-denoising","display_name":"image denoising","score":0.8550000190734863},{"id":"https://openalex.org/keywords/age-estimation","display_name":"age estimation","score":0.9890000224113464},{"id":"https://openalex.org/keywords/kernel-regression","display_name":"kernel regression","score":0.6740000247955322},{"id":"https://openalex.org/keywords/noise-decomposition","display_name":"noise decomposition","score":0.4569999873638153}],"concepts":[{"id":"https://openalex.org/C14948415","wikidata":"https://www.wikidata.org/wiki/Q7310972","display_name":"Relevance vector machine","level":3,"score":0.8289904594421387},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7044318318367004},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6686402559280396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6120189428329468},{"id":"https://openalex.org/C70259352","wikidata":"https://www.wikidata.org/wiki/Q1847839","display_name":"Robust regression","level":3,"score":0.5818913578987122},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5648431181907654},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5126746892929077},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4970262348651886},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49700072407722473},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4525354504585266},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44476330280303955},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3061140775680542},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.07833361625671387},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/cvpr.2010.5539861","doi":"https://doi.org/10.1109/cvpr.2010.5539861","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2010.5539861","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.190.5577","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.190.5577","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cfar.umd.edu/%7Erama/Publications/mitra_cvpr_2010.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.231.8694","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.231.8694","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cfar.umd.edu/%7Evashok/MyPapers/HighlySelectiveConf2010/cvpr_rob.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.374.8724","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.374.8724","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.merl.com/publications/docs/TR2010-080.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1633751774","https://openalex.org/W1746819321","https://openalex.org/W1819276697","https://openalex.org/W1969408578","https://openalex.org/W1986931325","https://openalex.org/W2006262236","https://openalex.org/W2012451168","https://openalex.org/W2032369048","https://openalex.org/W2039140324","https://openalex.org/W2047853863","https://openalex.org/W2078204800","https://openalex.org/W2097323375","https://openalex.org/W2106143125","https://openalex.org/W2113945798","https://openalex.org/W2119667497","https://openalex.org/W2123503110","https://openalex.org/W2147278565","https://openalex.org/W2148154358","https://openalex.org/W2149382413","https://openalex.org/W2156909104","https://openalex.org/W2158162711","https://openalex.org/W2163626514","https://openalex.org/W4211049957","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W7211289410","https://openalex.org/W2003481928","https://openalex.org/W2913141361","https://openalex.org/W2889476950","https://openalex.org/W2066890711","https://openalex.org/W2009086487","https://openalex.org/W2480763607","https://openalex.org/W2097027431","https://openalex.org/W2123747382","https://openalex.org/W2229601114"],"abstract_inverted_index":{"Kernel":[0],"regression":[1,50,63],"techniques":[2,51],"such":[3,25],"as":[4,26],"Relevance":[5],"Vector":[6,11],"Machine":[7],"(RVM)":[8],"regression,":[9],"Support":[10],"Regression":[12],"and":[13,33,87,122,161,167],"Gaussian":[14,89],"processes":[15],"are":[16],"widely":[17],"used":[18],"for":[19,107],"solving":[20,108],"many":[21],"computer":[22],"vision":[23],"problems":[24],"age,":[27],"head":[28],"pose,":[29],"3D":[30],"human":[31],"pose":[32],"lighting":[34],"estimation.":[35],"However,":[36],"the":[37,42,46,61,69,74,78,95,100,119,133,136,147,171,175,182],"presence":[38],"of":[39,60,174,181],"outliers":[40,67],"in":[41,68,77],"training":[43,70],"dataset":[44],"makes":[45],"estimates":[47],"from":[48],"these":[49],"unreliable.":[52],"In":[53,132],"this":[54,109],"paper,":[55],"we":[56],"propose":[57],"robust":[58,137,176],"versions":[59],"RVM":[62,79,120,138,144,177,183],"that":[64,149,180],"can":[65,151],"handle":[66],"dataset.":[71],"We":[72,92,103],"decompose":[73],"noise":[75,85,90,97],"term":[76,86],"formulation":[80],"into":[81],"a":[82,88,113,142,156],"(sparse)":[83],"outlier":[84,96],"term.":[91],"then":[93],"estimate":[94],"along":[98],"with":[99,146],"model":[101],"parameters.":[102],"present":[104],"two":[105],"approaches":[106],"estimation":[110,169],"problem:":[111],"(1)":[112],"Bayesian":[114,134],"approach,":[115,135],"which":[116],"essentially":[117,140],"follows":[118],"framework":[121],"(2)":[123],"an":[124],"optimization":[125],"approach":[126],"based":[127],"on":[128,164],"Basis":[129],"Pursuit":[130],"Denoising.":[131],"problem":[139,145],"becomes":[141],"bigger":[143],"advantage":[148],"it":[150],"be":[152],"solved":[153],"efficiently":[154],"by":[155],"fast":[157],"algorithm.":[158],"Empirical":[159],"evaluations,":[160],"real":[162],"experiments":[163],"image":[165],"de-noising":[166],"age":[168],"demonstrate":[170],"better":[172],"performance":[173],"algorithms":[178],"over":[179],"reg":[184],"ression.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":3}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}