{"id":"https://openalex.org/W2061890139","doi":"https://doi.org/10.1109/bigdata.2014.7004233","title":"Distributed class dependent feature analysis &amp;#x2014; A big data approach","display_name":"Distributed class dependent feature analysis &amp;#x2014; A big data approach","publication_year":2014,"publication_date":"2014-10-01","ids":{"openalex":"https://openalex.org/W2061890139","doi":"https://doi.org/10.1109/bigdata.2014.7004233","mag":"2061890139"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2014.7004233","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2014.7004233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Big Data (Big Data)","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/A5056226189","display_name":"Khoa Luu","orcid":"https://orcid.org/0000-0003-2104-0901"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Khoa Luu","raw_affiliation_strings":["Cylab Biometrics Center and Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cylab Biometrics Center and Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101087504","display_name":"Chenchen Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenchen Zhu","raw_affiliation_strings":["Cylab Biometrics Center and Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cylab Biometrics Center and Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057959136","display_name":"Marios Savvides","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marios Savvides","raw_affiliation_strings":["Cylab Biometrics Center and Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cylab Biometrics Center and Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9661,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.75355769,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"54","issue":null,"first_page":"201","last_page":"206"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10057","display_name":"Face and Expression Recognition","score":0.9988999962806702,"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/T10326","display_name":"Indoor and Outdoor Localization Technologies","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/computer-science","display_name":"Computer science","score":0.7930517792701721},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.6624632477760315},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6126725077629089},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.57501220703125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5387782454490662},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.44727614521980286},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4450226128101349},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.43826720118522644},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.4273836612701416},{"id":"https://openalex.org/keywords/rate-of-convergence","display_name":"Rate of convergence","score":0.4182397127151489},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.41295894980430603},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3882421851158142},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.35762667655944824},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3129516839981079},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.13805580139160156}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7930517792701721},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.6624632477760315},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6126725077629089},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.57501220703125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5387782454490662},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.44727614521980286},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4450226128101349},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.43826720118522644},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.4273836612701416},{"id":"https://openalex.org/C57869625","wikidata":"https://www.wikidata.org/wiki/Q1783502","display_name":"Rate of convergence","level":3,"score":0.4182397127151489},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.41295894980430603},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3882421851158142},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35762667655944824},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3129516839981079},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.13805580139160156},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2014.7004233","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2014.7004233","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W52367139","https://openalex.org/W1576445103","https://openalex.org/W1972715665","https://openalex.org/W2027805700","https://openalex.org/W2027922120","https://openalex.org/W2050080752","https://openalex.org/W2084716923","https://openalex.org/W2105594236","https://openalex.org/W2113296753","https://openalex.org/W2129812935","https://openalex.org/W2153663612","https://openalex.org/W2155904486","https://openalex.org/W2160547390","https://openalex.org/W2162419281","https://openalex.org/W2164278908","https://openalex.org/W2165828254","https://openalex.org/W2173213060","https://openalex.org/W2994340921","https://openalex.org/W3144858519","https://openalex.org/W4292363360","https://openalex.org/W6602099260","https://openalex.org/W6634343353","https://openalex.org/W6635552349","https://openalex.org/W6643530279","https://openalex.org/W6675691070","https://openalex.org/W6683908093","https://openalex.org/W6684893555"],"related_works":["https://openalex.org/W4390608645","https://openalex.org/W4247566972","https://openalex.org/W4394895745","https://openalex.org/W2960264696","https://openalex.org/W3090563135","https://openalex.org/W2497432351","https://openalex.org/W4206777497","https://openalex.org/W2910064364","https://openalex.org/W4200136508","https://openalex.org/W2499527417"],"abstract_inverted_index":{"Big":[0],"data":[1,23],"has":[2,36],"been":[3],"becoming":[4],"ubiquitous":[5],"and":[6,140,165,209,220,251],"applied":[7],"in":[8,21,27,63,101,168,178,181,198],"numerous":[9],"fields":[10],"recently.":[11],"The":[12,105],"challenges":[13],"to":[14,37,77,94,216,247],"solve":[15,78],"a":[16,31,48,71,83,156],"large-scale":[17,169,204],"machine":[18,33,85,244],"learning":[19,34,86],"problem":[20,130],"big":[22],"scenario":[24],"generally":[25],"lie":[26],"three":[28],"aspects.":[29],"Firstly,":[30],"proposed":[32,149],"algorithm":[35,69],"be":[38,152],"appropriated":[39],"for":[40,50],"the":[41,51,55,67,96,110,134,182,192,195,248],"distributed":[42,52,158,231],"optimization":[43,124],"problem.":[44,125],"Secondly,":[45],"it":[46,161],"needs":[47],"platform":[49],"implementation.":[53],"Finally,":[54],"communication":[56],"delays":[57],"different":[58],"machines":[59],"may":[60],"cause":[61],"problems":[62],"convergence":[64,73],"even":[65,254],"though":[66],"non-distributed":[68,249],"shows":[70,213],"good":[72],"rate.":[74],"In":[75,224],"order":[76],"these":[79],"challenges,":[80],"we":[81],"propose":[82],"new":[84],"approach":[87],"named":[88],"Distributed":[89],"Class-dependent":[90],"Feature":[91],"Analysis":[92],"(DCFA),":[93],"combine":[95],"advantages":[97],"of":[98,112,138,184,194,236],"sparse":[99],"representation":[100],"an":[102,118],"over-complete":[103],"dictionary.":[104],"classifier":[106],"is":[107,131],"based":[108],"on":[109,155,186,201,238],"estimation":[111],"class-specific":[113],"optimal":[114],"filters,":[115],"by":[116],"solving":[117],"l":[119],"<sub":[120],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[121],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[122],"-norm":[123],"We":[126],"demonstrate":[127],"how":[128],"this":[129],"solved":[132],"using":[133],"Alternating":[135],"Direction":[136],"Method":[137],"Multipliers":[139],"also":[141,190],"explore":[142],"relevant":[143],"convergency":[144],"details.":[145],"More":[146],"importantly,":[147],"our":[148,230],"framework":[150],"can":[151,252],"efficiently":[153],"implemented":[154],"robust":[157],"framework.":[159],"Thus,":[160],"improves":[162],"both":[163],"accuracy":[164],"computational":[166,226],"time":[167,227],"databases.":[170],"Our":[171],"method":[172,232],"achieves":[173,233],"very":[174],"high":[175,234],"classification":[176],"accuracies":[177],"face":[179],"recognition":[180,200,222],"presence":[183],"occlusions":[185],"AR":[187],"database.":[188],"It":[189,211],"outperforms":[191],"state":[193],"art":[196],"methods":[197],"object":[199,205],"two":[202],"challenging":[203],"databases,":[206],"i.e.":[207],"Caltech101":[208],"Caltech256.":[210],"hence":[212],"its":[214],"applicability":[215],"general":[217],"computer":[218],"vision":[219],"pattern":[221],"problems.":[223],"addition,":[225],"experiments":[228],"show":[229],"speedup":[235],"7.85x":[237],"Caltech256":[239],"databases":[240],"with":[241,256],"just":[242],"10":[243],"nodes":[245],"compared":[246],"version":[250],"gain":[253],"more":[255,257],"computing":[258],"resources.":[259]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
