{"id":"https://openalex.org/W2057495020","doi":"https://doi.org/10.1145/2499907.2499911","title":"Exploiting fisher and fukunaga-koontz transforms in chernoff dimensionality reduction","display_name":"Exploiting fisher and fukunaga-koontz transforms in chernoff dimensionality reduction","publication_year":2013,"publication_date":"2013-07-01","ids":{"openalex":"https://openalex.org/W2057495020","doi":"https://doi.org/10.1145/2499907.2499911","mag":"2057495020"},"language":"en","primary_location":{"id":"doi:10.1145/2499907.2499911","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2499907.2499911","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from 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/A5101892033","display_name":"Jing Peng","orcid":"https://orcid.org/0000-0001-8822-310X"},"institutions":[{"id":"https://openalex.org/I166088655","display_name":"Montclair State University","ror":"https://ror.org/01nxc2t48","country_code":"US","type":"education","lineage":["https://openalex.org/I166088655"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jing Peng","raw_affiliation_strings":["Montclair State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Montclair State University","institution_ids":["https://openalex.org/I166088655"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089593576","display_name":"Guna Seetharaman","orcid":null},"institutions":[{"id":"https://openalex.org/I1280414376","display_name":"United States Air Force Research Laboratory","ror":"https://ror.org/02e2egq70","country_code":"US","type":"facility","lineage":["https://openalex.org/I1280414376","https://openalex.org/I1330347796","https://openalex.org/I4210102105","https://openalex.org/I4389425425"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guna Seetharaman","raw_affiliation_strings":["Air Force Research Lab","air force research lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Air Force Research Lab","institution_ids":["https://openalex.org/I1280414376"]},{"raw_affiliation_string":"air force research lab","institution_ids":["https://openalex.org/I1280414376"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100380499","display_name":"Wei Fan","orcid":"https://orcid.org/0000-0002-0342-6272"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Fan","raw_affiliation_strings":["Huawei Noah Ark Lab, Hong Kong","Huawei Noah Ark Lab, Hong Kong#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huawei Noah Ark Lab, Hong Kong","institution_ids":["https://openalex.org/I2250955327"]},{"raw_affiliation_string":"Huawei Noah Ark Lab, Hong Kong#TAB#","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090253347","display_name":"Aparna S. Varde","orcid":"https://orcid.org/0000-0002-3170-2510"},"institutions":[{"id":"https://openalex.org/I166088655","display_name":"Montclair State University","ror":"https://ror.org/01nxc2t48","country_code":"US","type":"education","lineage":["https://openalex.org/I166088655"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aparna Varde","raw_affiliation_strings":["Montclair State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Montclair State University","institution_ids":["https://openalex.org/I166088655"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.12530439,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"7","issue":"2","first_page":"1","last_page":"25"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9998000264167786,"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":0.9998000264167786,"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/T10320","display_name":"Neural Networks and Applications","score":0.9962000250816345,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9865999817848206,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.7888228893280029},{"id":"https://openalex.org/keywords/heteroscedasticity","display_name":"Heteroscedasticity","score":0.7701477408409119},{"id":"https://openalex.org/keywords/chernoff-bound","display_name":"Chernoff bound","score":0.5959569811820984},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5718660354614258},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.528624415397644},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4811490774154663},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45595553517341614},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44432199001312256},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4140920341014862},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.41212019324302673},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34915468096733093},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2702433466911316},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2179160714149475}],"concepts":[{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.7888228893280029},{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.7701477408409119},{"id":"https://openalex.org/C14539891","wikidata":"https://www.wikidata.org/wiki/Q1070305","display_name":"Chernoff bound","level":2,"score":0.5959569811820984},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5718660354614258},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.528624415397644},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4811490774154663},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45595553517341614},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44432199001312256},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4140920341014862},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.41212019324302673},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34915468096733093},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2702433466911316},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2179160714149475},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2499907.2499911","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2499907.2499911","pdf_url":null,"source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7099999785423279,"display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320338294","display_name":"Air Force Research Laboratory","ror":"https://ror.org/02e2egq70"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W128984794","https://openalex.org/W1528620860","https://openalex.org/W1563088657","https://openalex.org/W1581253957","https://openalex.org/W1583700199","https://openalex.org/W1583837637","https://openalex.org/W1604548007","https://openalex.org/W1770825568","https://openalex.org/W1793242010","https://openalex.org/W1975595700","https://openalex.org/W1977271127","https://openalex.org/W1980505254","https://openalex.org/W1985078322","https://openalex.org/W1985852691","https://openalex.org/W2006087861","https://openalex.org/W2030328665","https://openalex.org/W2046368494","https://openalex.org/W2073039128","https://openalex.org/W2088900896","https://openalex.org/W2098947662","https://openalex.org/W2101408147","https://openalex.org/W2105055468","https://openalex.org/W2106079515","https://openalex.org/W2107542203","https://openalex.org/W2111574755","https://openalex.org/W2117513046","https://openalex.org/W2121384726","https://openalex.org/W2121647436","https://openalex.org/W2122111042","https://openalex.org/W2128716185","https://openalex.org/W2130418330","https://openalex.org/W2135346934","https://openalex.org/W2137199074","https://openalex.org/W2138451337","https://openalex.org/W2146820706","https://openalex.org/W2156571432","https://openalex.org/W2160817396","https://openalex.org/W2163406054","https://openalex.org/W2171347282","https://openalex.org/W2171849959","https://openalex.org/W2209413969","https://openalex.org/W2398147772","https://openalex.org/W2488133945","https://openalex.org/W2596164567","https://openalex.org/W2911640577","https://openalex.org/W3005272312","https://openalex.org/W4236965008","https://openalex.org/W4297984052"],"related_works":["https://openalex.org/W1676609285","https://openalex.org/W2114217318","https://openalex.org/W2794812819","https://openalex.org/W2587881214","https://openalex.org/W3104072235","https://openalex.org/W3036945320","https://openalex.org/W2370263288","https://openalex.org/W2169311637","https://openalex.org/W2395040056","https://openalex.org/W2052339338"],"abstract_inverted_index":{"Knowledge":[0],"discovery":[1,22],"from":[2,107],"big":[3,11],"data":[4,12,140,159],"demands":[5],"effective":[6],"representation":[7],"of":[8,46,70,91,138,157],"data.":[9,52,76,169],"However,":[10,40],"are":[13],"often":[14],"characterized":[15],"by":[16],"high":[17],"dimensionality,":[18],"which":[19],"makes":[20],"knowledge":[21],"more":[23],"difficult.":[24],"Many":[25],"techniques":[26],"for":[27,60,165],"dimensionality":[28,62],"reudction":[29],"have":[30],"been":[31,65,82],"proposed,":[32],"including":[33],"well-known":[34],"Fisher's":[35],"Linear":[36],"Discriminant":[37],"Analysis":[38],"(LDA).":[39],"the":[41,51,57,75,78,86,108,116,120,139,155,158,168],"Fisher":[42,121],"criterion":[43,59,80,118],"is":[44,68,95,145],"incapable":[45],"dealing":[47],"with":[48],"heteroscedasticity":[49,166],"in":[50,74,125,161,167],"A":[53],"technique":[54],"based":[55],"on":[56,150],"Chernoff":[58,79,109,117],"linear":[61,128],"reduction":[63],"has":[64,81],"proposed":[66,136],"that":[67,115,133],"capable":[69],"exploiting":[71],"heteroscedastic":[72],"information":[73],"While":[77],"shown":[83],"to":[84,152,163],"outperform":[85],"Fisher's,":[87],"a":[88,134],"clear":[89],"understanding":[90],"its":[92,127],"exact":[93],"behavior":[94],"lacking.":[96],"In":[97,111],"this":[98],"article,":[99],"we":[100,113,131,171],"show":[101,114,132],"precisely":[102],"what":[103],"can":[104],"be":[105],"expected":[106],"criterion.":[110],"particular,":[112],"exploits":[119],"and":[122],"Fukunaga-Koontz":[123],"transforms":[124],"computing":[126],"discriminants.":[129],"Furthermore,":[130],"recently":[135],"decomposition":[137,156],"space":[141,160],"into":[142],"four":[143],"subspaces":[144],"incomplete.":[146],"We":[147],"provide":[148,172],"arguments":[149],"how":[151],"best":[153],"enrich":[154],"order":[162],"account":[164],"Finally,":[170],"experimental":[173],"results":[174],"validating":[175],"our":[176],"theoretical":[177],"analysis.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
