{"id":"https://openalex.org/W2767196655","doi":"https://doi.org/10.23919/eusipco.2017.8081393","title":"Initializing probabilistic linear discriminant analysis","display_name":"Initializing probabilistic linear discriminant analysis","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2767196655","doi":"https://doi.org/10.23919/eusipco.2017.8081393","mag":"2767196655"},"language":"en","primary_location":{"id":"doi:10.23919/eusipco.2017.8081393","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2017.8081393","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 25th European Signal Processing Conference (EUSIPCO)","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/A5081021169","display_name":"Stylianos Moschoglou","orcid":"https://orcid.org/0000-0001-7421-1335"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Stylianos Moschoglou","raw_affiliation_strings":["Department of Computing, Imperial College Londo, London, UK"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Imperial College Londo, London, UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017437981","display_name":"Mihalis A. Nicolaou","orcid":"https://orcid.org/0000-0001-9175-477X"},"institutions":[{"id":"https://openalex.org/I55521800","display_name":"Goldsmiths University of London","ror":"https://ror.org/01khx4a30","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I55521800"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mihalis Nicolaou","raw_affiliation_strings":["Department of Computing, Goldsmiths University of London, London, UK"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Goldsmiths University of London, London, UK","institution_ids":["https://openalex.org/I55521800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050734738","display_name":"Yannis Panagakis","orcid":"https://orcid.org/0000-0003-0153-5210"},"institutions":[{"id":"https://openalex.org/I60488453","display_name":"Middlesex University","ror":"https://ror.org/01rv4p989","country_code":"GB","type":"education","lineage":["https://openalex.org/I60488453"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yannis Panagakis","raw_affiliation_strings":["Department of Computing, Middlesex University London, London, UK"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Middlesex University London, London, UK","institution_ids":["https://openalex.org/I60488453"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080553022","display_name":"Stefanos Zafeiriou","orcid":"https://orcid.org/0000-0002-5222-1740"},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Stefanos Zafeiriou","raw_affiliation_strings":["Department of Computing, Imperial College Londo, London, UK"],"affiliations":[{"raw_affiliation_string":"Department of Computing, Imperial College Londo, London, UK","institution_ids":["https://openalex.org/I47508984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5081021169"],"corresponding_institution_ids":["https://openalex.org/I47508984"],"apc_list":null,"apc_paid":null,"fwci":0.0923,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.48871795,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"14","issue":null,"first_page":"1175","last_page":"1179"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9991000294685364,"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":0.9991000294685364,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9868000149726868,"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/initialization","display_name":"Initialization","score":0.8145864009857178},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7801538705825806},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7756326794624329},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6871477365493774},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6665149927139282},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6237486600875854},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5879601836204529},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.5618252754211426},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5117475986480713},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4434453248977661},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.42624813318252563},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3334682583808899},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17884746193885803}],"concepts":[{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.8145864009857178},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7801538705825806},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7756326794624329},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6871477365493774},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6665149927139282},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6237486600875854},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5879601836204529},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.5618252754211426},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5117475986480713},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4434453248977661},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.42624813318252563},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3334682583808899},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17884746193885803},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.23919/eusipco.2017.8081393","is_oa":false,"landing_page_url":"https://doi.org/10.23919/eusipco.2017.8081393","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 25th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.gold.ac.uk:21283","is_oa":false,"landing_page_url":"https://research.gold.ac.uk/view/goldsmiths/Nicolaou=3AM=2E_A=2E=3A=3A.html>;","pdf_url":null,"source":{"id":"https://openalex.org/S4306400496","display_name":"Goldsmiths (University of London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I166337079","host_organization_name":"Queen Mary University of London","host_organization_lineage":["https://openalex.org/I166337079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Published Version","raw_type":"public"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6600000262260437}],"awards":[{"id":"https://openalex.org/G4418387473","display_name":null,"funder_award_id":"1792614","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1565908848","https://openalex.org/W1589137271","https://openalex.org/W1669104078","https://openalex.org/W1923565741","https://openalex.org/W1944008203","https://openalex.org/W2012352340","https://openalex.org/W2030733315","https://openalex.org/W2045079045","https://openalex.org/W2049633694","https://openalex.org/W2100235303","https://openalex.org/W2103633133","https://openalex.org/W2103731208","https://openalex.org/W2121812409","https://openalex.org/W2124161253","https://openalex.org/W2125027820","https://openalex.org/W2125290066","https://openalex.org/W2127928018","https://openalex.org/W2128716185","https://openalex.org/W2136111243","https://openalex.org/W2144903813","https://openalex.org/W2146610201","https://openalex.org/W2159786793","https://openalex.org/W2161305109","https://openalex.org/W2912990735","https://openalex.org/W3099514962","https://openalex.org/W4250857377","https://openalex.org/W6676047094","https://openalex.org/W6680375596","https://openalex.org/W6681437887"],"related_works":["https://openalex.org/W2052589448","https://openalex.org/W2765337000","https://openalex.org/W1964760042","https://openalex.org/W3104072235","https://openalex.org/W2391447249","https://openalex.org/W2312955079","https://openalex.org/W2350996391","https://openalex.org/W2066119650","https://openalex.org/W2059615944","https://openalex.org/W4224057882"],"abstract_inverted_index":{"Component":[0,53],"Analysis":[1,58],"(CA)":[2],"consists":[3],"of":[4,7,35,46,67,89,94,108,143,151,164],"a":[5,126,136,149],"set":[6,150],"statistical":[8],"techniques":[9,74],"that":[10,17],"decompose":[11],"data":[12,90],"to":[13,20,80,113,129],"appropriate":[14],"latent":[15],"components":[16],"are":[18],"relevant":[19],"the":[21,29,44,68,87,105,109,131,144,154,162,165],"task-at-hand":[22],"(e.g.,":[23,50],"clustering,":[24],"segmentation,":[25],"classification,":[26],"alignment).":[27],"During":[28],"past":[30],"few":[31],"years,":[32],"an":[33,98],"explosion":[34],"research":[36],"in":[37,78,92,101,111,117,133,135],"probabilistic":[38],"CA":[39,73],"has":[40],"been":[41],"witnessed,":[42],"with":[43],"introduction":[45],"several":[47],"novel":[48,127],"methods":[49],"Probabilistic":[51,55,60],"Principal":[52],"Analysis,":[54],"Linear":[56],"Discriminant":[57],"(PLDA),":[59],"Canonical":[61],"Correlation":[62],"Analysis).":[63],"PLDA":[64,102,134,167],"constitutes":[65],"one":[66],"most":[69],"widely":[70],"used":[71],"supervised":[72],"which":[75,158],"is":[76,104,146,159],"utilized":[77],"order":[79,112],"extract":[81],"suitable,":[82],"distinct":[83],"subspaces":[84],"by":[85,161],"exploiting":[86],"knowledge":[88],"annotated":[91],"terms":[93],"different":[95],"labels.":[96],"Nevertheless,":[97],"inherent":[99],"difficulty":[100],"variants":[103],"proper":[106],"initialization":[107],"parameters":[110,132],"avoid":[114],"ending":[115],"up":[116],"poor":[118],"local":[119],"maxima.":[120],"In":[121],"this":[122],"light,":[123],"we":[124],"propose":[125],"method":[128],"initialize":[130],"consistent":[137],"and":[138],"robust":[139],"way.":[140],"The":[141],"performance":[142],"algorithm":[145],"demonstrated":[147],"via":[148],"experiments":[152],"on":[153],"modified":[155],"XM2VTS":[156],"database,":[157],"provided":[160],"authors":[163],"original":[166],"model.":[168]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
