{"id":"https://openalex.org/W2145901830","doi":"https://doi.org/10.1109/lsp.2011.2127474","title":"Mixture Subclass Discriminant Analysis","display_name":"Mixture Subclass Discriminant Analysis","publication_year":2011,"publication_date":"2011-03-15","ids":{"openalex":"https://openalex.org/W2145901830","doi":"https://doi.org/10.1109/lsp.2011.2127474","mag":"2145901830"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2011.2127474","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2011.2127474","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://zenodo.org/record/3433732","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055821226","display_name":"Nikolaos Gkalelis","orcid":"https://orcid.org/0000-0001-6741-3334"},"institutions":[{"id":"https://openalex.org/I4210093649","display_name":"Information Technologies Institute","ror":"https://ror.org/0069akp70","country_code":"GR","type":"nonprofit","lineage":["https://openalex.org/I4210093649"]},{"id":"https://openalex.org/I4210134249","display_name":"Centre for Research and Technology Hellas","ror":"https://ror.org/03bndpq63","country_code":"GR","type":"facility","lineage":["https://openalex.org/I4210134249"]},{"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","GR"],"is_corresponding":false,"raw_author_name":"N Gkalelis","raw_affiliation_strings":["Centre of Research and Technology Hellas (CERTH), Informatics and Telematics Institute, Thermi, Greece","Department of Electrical and Electronic Engineering, Imperial College London, London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre of Research and Technology Hellas (CERTH), Informatics and Telematics Institute, Thermi, Greece","institution_ids":["https://openalex.org/I4210134249","https://openalex.org/I4210093649"]},{"raw_affiliation_string":"Department of Electrical and Electronic Engineering, Imperial College London, London, UK","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059096027","display_name":"Vasileios Mezaris","orcid":"https://orcid.org/0000-0002-0121-4364"},"institutions":[{"id":"https://openalex.org/I4210093649","display_name":"Information Technologies Institute","ror":"https://ror.org/0069akp70","country_code":"GR","type":"nonprofit","lineage":["https://openalex.org/I4210093649"]},{"id":"https://openalex.org/I4210134249","display_name":"Centre for Research and Technology Hellas","ror":"https://ror.org/03bndpq63","country_code":"GR","type":"facility","lineage":["https://openalex.org/I4210134249"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"V Mezaris","raw_affiliation_strings":["Centre of Research and Technology Hellas (CERTH), Informatics and Telematics Institute, Thermi, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre of Research and Technology Hellas (CERTH), Informatics and Telematics Institute, Thermi, Greece","institution_ids":["https://openalex.org/I4210134249","https://openalex.org/I4210093649"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084122016","display_name":"Ioannis Kompatsiaris","orcid":"https://orcid.org/0000-0001-6447-9020"},"institutions":[{"id":"https://openalex.org/I4210093649","display_name":"Information Technologies Institute","ror":"https://ror.org/0069akp70","country_code":"GR","type":"nonprofit","lineage":["https://openalex.org/I4210093649"]},{"id":"https://openalex.org/I4210134249","display_name":"Centre for Research and Technology Hellas","ror":"https://ror.org/03bndpq63","country_code":"GR","type":"facility","lineage":["https://openalex.org/I4210134249"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"I Kompatsiaris","raw_affiliation_strings":["Centre of Research and Technology Hellas (CERTH), Informatics and Telematics Institute, Thermi, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Centre of Research and Technology Hellas (CERTH), Informatics and Telematics Institute, Thermi, Greece","institution_ids":["https://openalex.org/I4210134249","https://openalex.org/I4210093649"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.2339,"has_fulltext":false,"cited_by_count":64,"citation_normalized_percentile":{"value":0.96490432,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":"18","issue":"5","first_page":"319","last_page":"322"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9943000078201294,"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":0.9943000078201294,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9672999978065491,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.949999988079071,"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/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.8438162803649902},{"id":"https://openalex.org/keywords/optimal-discriminant-analysis","display_name":"Optimal discriminant analysis","score":0.751013994216919},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7117593288421631},{"id":"https://openalex.org/keywords/kernel-fisher-discriminant-analysis","display_name":"Kernel Fisher discriminant analysis","score":0.7098033428192139},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6376742124557495},{"id":"https://openalex.org/keywords/homoscedasticity","display_name":"Homoscedasticity","score":0.633088231086731},{"id":"https://openalex.org/keywords/subclass","display_name":"Subclass","score":0.6274704933166504},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.6167563796043396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5741401314735413},{"id":"https://openalex.org/keywords/multiple-discriminant-analysis","display_name":"Multiple discriminant analysis","score":0.5024158954620361},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4943845272064209},{"id":"https://openalex.org/keywords/discriminant-function-analysis","display_name":"Discriminant function analysis","score":0.44415390491485596},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.4399903118610382},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.42169812321662903},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.31910213828086853},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.26102709770202637},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2528412342071533},{"id":"https://openalex.org/keywords/heteroscedasticity","display_name":"Heteroscedasticity","score":0.09967878460884094},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.08641201257705688}],"concepts":[{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.8438162803649902},{"id":"https://openalex.org/C104500394","wikidata":"https://www.wikidata.org/wiki/Q17104912","display_name":"Optimal discriminant analysis","level":3,"score":0.751013994216919},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7117593288421631},{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.7098033428192139},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6376742124557495},{"id":"https://openalex.org/C104409967","wikidata":"https://www.wikidata.org/wiki/Q1054836","display_name":"Homoscedasticity","level":3,"score":0.633088231086731},{"id":"https://openalex.org/C2776194381","wikidata":"https://www.wikidata.org/wiki/Q3965271","display_name":"Subclass","level":3,"score":0.6274704933166504},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.6167563796043396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5741401314735413},{"id":"https://openalex.org/C58596280","wikidata":"https://www.wikidata.org/wiki/Q28324849","display_name":"Multiple discriminant analysis","level":3,"score":0.5024158954620361},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4943845272064209},{"id":"https://openalex.org/C41771347","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Discriminant function analysis","level":2,"score":0.44415390491485596},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4399903118610382},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.42169812321662903},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.31910213828086853},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.26102709770202637},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2528412342071533},{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.09967878460884094},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.08641201257705688},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C203014093","wikidata":"https://www.wikidata.org/wiki/Q101929","display_name":"Immunology","level":1,"score":0.0},{"id":"https://openalex.org/C159654299","wikidata":"https://www.wikidata.org/wiki/Q79460","display_name":"Antibody","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/lsp.2011.2127474","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2011.2127474","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.727.4789","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.727.4789","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mklab.iti.gr/files/spl11_preprint.pdf","raw_type":"text"},{"id":"pmh:oai:zenodo.org:3433732","is_oa":true,"landing_page_url":"https://zenodo.org/record/3433732","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:zenodo.org:3433732","is_oa":true,"landing_page_url":"https://zenodo.org/record/3433732","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1510147702","https://openalex.org/W1614659291","https://openalex.org/W1770825568","https://openalex.org/W1965900320","https://openalex.org/W2117513046","https://openalex.org/W2121647436","https://openalex.org/W2135346934","https://openalex.org/W2137170986","https://openalex.org/W2137611320","https://openalex.org/W2143340430","https://openalex.org/W2146439239","https://openalex.org/W2158114849","https://openalex.org/W2164552042"],"related_works":["https://openalex.org/W2025089370","https://openalex.org/W2162393942","https://openalex.org/W2147350351","https://openalex.org/W4236598502","https://openalex.org/W2771098736","https://openalex.org/W1992809818","https://openalex.org/W2383839323","https://openalex.org/W2379687477","https://openalex.org/W4229567225","https://openalex.org/W4238857462"],"abstract_inverted_index":{"In":[0,19],"this":[1,70],"letter,":[2],"mixture":[3],"subclass":[4,13,30,93],"discriminant":[5,14,40,63],"analysis":[6,15,64,71],"(MSDA)":[7],"that":[8,24,42],"alleviates":[9],"two":[10],"shortcomings":[11],"of":[12,61,77,88,102],"(SDA)":[16],"is":[17,22],"proposed.":[18],"particular,":[20],"it":[21],"shown":[23],"for":[25],"data":[26,89],"with":[27,90],"Gaussian":[28,91],"homoscedastic":[29,92],"structure":[31],"a)":[32],"SDA":[33,78],"does":[34],"not":[35,54],"guarantee":[36],"to":[37,85],"provide":[38],"the":[39,44,49,58,62,74,98],"subspace":[41],"minimizes":[43],"Bayes":[45],"error,":[46],"and,":[47],"b)":[48],"sample":[50],"covariance":[51],"matrix":[52],"can":[53],"be":[55],"used":[56],"as":[57],"minimization":[59],"metric":[60],"stability":[65],"criterion":[66],"(DSC).":[67],"Based":[68],"on":[69],"MSDA":[72],"modifies":[73],"objective":[75],"function":[76],"and":[79],"utilizes":[80],"a":[81],"novel":[82],"partitioning":[83],"procedure":[84],"aid":[86],"discrimination":[87],"structure.":[94],"Experimental":[95],"results":[96],"confirm":[97],"improved":[99],"classification":[100],"performance":[101],"MSDA.":[103]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":5},{"year":2015,"cited_by_count":7},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":10},{"year":2012,"cited_by_count":5}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}