{"id":"https://openalex.org/W2148730406","doi":"https://doi.org/10.1109/cvpr.2003.1211430","title":"Feature selection by maximum marginal diversity: optimality and implications for visual recognition","display_name":"Feature selection by maximum marginal diversity: optimality and implications for visual recognition","publication_year":2003,"publication_date":"2003-11-21","ids":{"openalex":"https://openalex.org/W2148730406","doi":"https://doi.org/10.1109/cvpr.2003.1211430","mag":"2148730406"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2003.1211430","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2003.1211430","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.","raw_type":"proceedings-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/A5043325212","display_name":"Nuno Vasconcelos","orcid":"https://orcid.org/0000-0002-9024-4302"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California, San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"N. Vasconcelos","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of California, San Diego, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of California, San Diego, USA","institution_ids":["https://openalex.org/I36258959"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5043325212"],"corresponding_institution_ids":["https://openalex.org/I36258959"],"apc_list":null,"apc_paid":null,"fwci":7.2678,"has_fulltext":false,"cited_by_count":63,"citation_normalized_percentile":{"value":0.97628444,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"I","last_page":"762"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9955000281333923,"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":0.9955000281333923,"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":0.9912999868392944,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9894999861717224,"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/infomax","display_name":"Infomax","score":0.8932434320449829},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7682000398635864},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7383863925933838},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.6245625615119934},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.537303626537323},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5100246071815491},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5010871887207031},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4413701295852661},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.439005970954895},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34693706035614014}],"concepts":[{"id":"https://openalex.org/C153402090","wikidata":"https://www.wikidata.org/wiki/Q6030480","display_name":"Infomax","level":4,"score":0.8932434320449829},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7682000398635864},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7383863925933838},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6245625615119934},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.537303626537323},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5100246071815491},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5010871887207031},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4413701295852661},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.439005970954895},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34693706035614014},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/cvpr.2003.1211430","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2003.1211430","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.1011.3834","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1011.3834","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://papers.nips.cc/paper/2169-feature-selection-by-maximum-marginal-diversity.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.140.7879","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.7879","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://books.nips.cc/papers/files/nips15/VS14.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.85.6096","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.85.6096","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.svcl.ucsd.edu/publications/conference/2003/cvpr03/cvpr03.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.87.5653","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.87.5653","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.svcl.ucsd.edu/publications/conference/2002/nips02/nips02.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1556076050","https://openalex.org/W1624804034","https://openalex.org/W2099111195","https://openalex.org/W2108384452","https://openalex.org/W2121647436","https://openalex.org/W2122925692","https://openalex.org/W2123977795","https://openalex.org/W2127578227","https://openalex.org/W2130046005","https://openalex.org/W2134181001","https://openalex.org/W2138451337","https://openalex.org/W2139643804","https://openalex.org/W2142809429","https://openalex.org/W2148633389","https://openalex.org/W2163055872","https://openalex.org/W2399703712","https://openalex.org/W3214102110","https://openalex.org/W6633258933","https://openalex.org/W6679985256","https://openalex.org/W6680389264","https://openalex.org/W6683855484","https://openalex.org/W6781870204","https://openalex.org/W6804270185"],"related_works":["https://openalex.org/W2350751952","https://openalex.org/W1999647744","https://openalex.org/W2362114017","https://openalex.org/W3147024994","https://openalex.org/W2063246903","https://openalex.org/W2374055396","https://openalex.org/W1978302214","https://openalex.org/W2021817983","https://openalex.org/W3008559849","https://openalex.org/W2371177901"],"abstract_inverted_index":{"We":[0,178],"have":[1],"recently":[2],"shown":[3],"that:":[4,185],"1)":[5,117,186],"the":[6,10,25,43,48,52,74,92,95,99,110,125,144,158,165,173,183,193,223],"infomax":[7,40,96,127],"principle":[8,75,101],"for":[9,83,106,142,172,192,235],"organization":[11],"of":[12,55,76,98,112,139,149,154,160,175,196,200,228],"perceptual":[13],"systems":[14],"leads":[15],"to":[16,170],"visual":[17,113,176,229],"recognition":[18,194],"architectures":[19],"that":[20,134,164,206,231],"are":[21,146,168,207,226,232],"nearly":[22],"optimal":[23,126],"in":[24,39,109,152],"minimum":[26],"Bayes":[27],"error":[28],"sense,":[29],"and":[30,58,67,102,128,131,221],"2)":[31,132,202],"a":[32,64,120,137,189,197],"quantity":[33,66],"which":[34,143],"plays":[35],"an":[36],"important":[37],"role":[38],"solutions":[41],"is":[42,63,136,188],"marginal":[44,78],"diversity":[45,79],"(MD)":[46],"-":[47],"average":[49],"distance":[50],"between":[51,124],"class-conditional":[53],"density":[54],"each":[56],"feature":[57,85,107],"their":[59],"mean.":[60],"Since":[61],"MD":[62,187],"discriminant":[65,84,209],"can":[68],"be":[69],"computed":[70],"with":[71,213],"great":[72],"efficiency,":[73],"maximum":[77],"(MMD)":[80],"was":[81],"suggested":[82],"selection.":[86],"In":[87,115],"this":[88,150],"paper,":[89],"we":[90,118],"study":[91],"optimality":[93],"(in":[94],"sense)":[97],"MMD":[100,129,203],"analyze":[103],"its":[104],"effectiveness":[105],"selection":[108],"context":[111],"recognition.":[114,177],"particular,":[116],"derive":[119],"close":[121],"form":[122],"relation":[123],"solutions,":[130],"show":[133],"there":[135],"family":[138,151],"classification":[140],"problems":[141],"two":[145],"identical.":[147],"Examination":[148],"light":[153],"recent":[155],"studies":[156],"on":[157],"statistics":[159],"natural":[161],"images":[162],"suggests":[163],"equivalence":[166],"conditions":[167],"likely":[169],"hold":[171],"problem":[174],"present":[179],"experimental":[180],"evidence":[181],"supporting":[182],"conclusions":[184],"good":[190],"predictor":[191],"ability":[195],"given":[198],"set":[199],"features;":[201],"produces":[204],"features":[205,225],"more":[208],"than":[210],"those":[211],"obtained":[212],"currently":[214],"predominant":[215],"criteria":[216],"such":[217],"as":[218],"energy":[219],"compaction;":[220],"3)":[222],"extracted":[224],"detectors":[227],"attributes":[230],"perceptually":[233],"relevant":[234],"low-level":[236],"image":[237],"classification.":[238]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}