{"id":"https://openalex.org/W2766846512","doi":"https://doi.org/10.1145/3123266.3123401","title":"Incremental Accelerated Kernel Discriminant Analysis","display_name":"Incremental Accelerated Kernel Discriminant Analysis","publication_year":2017,"publication_date":"2017-10-20","ids":{"openalex":"https://openalex.org/W2766846512","doi":"https://doi.org/10.1145/3123266.3123401","mag":"2766846512"},"language":"en","primary_location":{"id":"doi:10.1145/3123266.3123401","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3123266.3123401","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM international conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://zenodo.org/record/1050373","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"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Nikolaos Gkalelis","raw_affiliation_strings":["CERTH-ITI, Thessaloniki, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CERTH-ITI, Thessaloniki, Greece","institution_ids":["https://openalex.org/I4210093649","https://openalex.org/I4210134249"]}]},{"author_position":"last","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":"Vasileios Mezaris","raw_affiliation_strings":["CERTH-ITI, Thessaloniki, Greece"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CERTH-ITI, Thessaloniki, Greece","institution_ids":["https://openalex.org/I4210093649","https://openalex.org/I4210134249"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1847,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.5864713,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1575","last_page":"1583"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9944000244140625,"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":0.9944000244140625,"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/T13717","display_name":"Advanced Algorithms and Applications","score":0.9848999977111816,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T10320","display_name":"Neural Networks and Applications","score":0.973800003528595,"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/cholesky-decomposition","display_name":"Cholesky decomposition","score":0.8090173602104187},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.7511587738990784},{"id":"https://openalex.org/keywords/kernel-fisher-discriminant-analysis","display_name":"Kernel Fisher discriminant analysis","score":0.6872625946998596},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.6243122816085815},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6082534790039062},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5835665464401245},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5702310800552368},{"id":"https://openalex.org/keywords/block-matrix","display_name":"Block matrix","score":0.5578095316886902},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.55699622631073},{"id":"https://openalex.org/keywords/polynomial-kernel","display_name":"Polynomial kernel","score":0.5216705799102783},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.5187113881111145},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5155158042907715},{"id":"https://openalex.org/keywords/qr-decomposition","display_name":"QR decomposition","score":0.5106959939002991},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.4697107672691345},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.46105945110321045},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.44755250215530396},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.4422224462032318},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4389876425266266},{"id":"https://openalex.org/keywords/eigendecomposition-of-a-matrix","display_name":"Eigendecomposition of a matrix","score":0.43310296535491943},{"id":"https://openalex.org/keywords/variable-kernel-density-estimation","display_name":"Variable kernel density estimation","score":0.43014243245124817},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3878519833087921},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3397129774093628},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.11717289686203003}],"concepts":[{"id":"https://openalex.org/C34727166","wikidata":"https://www.wikidata.org/wiki/Q515375","display_name":"Cholesky decomposition","level":3,"score":0.8090173602104187},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.7511587738990784},{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.6872625946998596},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6243122816085815},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6082534790039062},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5835665464401245},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5702310800552368},{"id":"https://openalex.org/C85817219","wikidata":"https://www.wikidata.org/wiki/Q884772","display_name":"Block matrix","level":3,"score":0.5578095316886902},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.55699622631073},{"id":"https://openalex.org/C160446489","wikidata":"https://www.wikidata.org/wiki/Q7226642","display_name":"Polynomial kernel","level":4,"score":0.5216705799102783},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.5187113881111145},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5155158042907715},{"id":"https://openalex.org/C188060507","wikidata":"https://www.wikidata.org/wiki/Q653242","display_name":"QR decomposition","level":3,"score":0.5106959939002991},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.4697107672691345},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.46105945110321045},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.44755250215530396},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.4422224462032318},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4389876425266266},{"id":"https://openalex.org/C169756996","wikidata":"https://www.wikidata.org/wiki/Q194919","display_name":"Eigendecomposition of a matrix","level":3,"score":0.43310296535491943},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.43014243245124817},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3878519833087921},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3397129774093628},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.11717289686203003},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3123266.3123401","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3123266.3123401","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 25th ACM international conference on Multimedia","raw_type":"proceedings-article"},{"id":"pmh:oai:zenodo.org:1050373","is_oa":true,"landing_page_url":"https://zenodo.org/record/1050373","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencePaper"}],"best_oa_location":{"id":"pmh:oai:zenodo.org:1050373","is_oa":true,"landing_page_url":"https://zenodo.org/record/1050373","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferencePaper"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.75}],"awards":[{"id":"https://openalex.org/G985420103","display_name":null,"funder_award_id":"H2020-732665 EMMA","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320332999","display_name":"Horizon 2020 Framework Programme","ror":"https://ror.org/00k4n6c32"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W1480487340","https://openalex.org/W1660353176","https://openalex.org/W1722318740","https://openalex.org/W1770825568","https://openalex.org/W1965534165","https://openalex.org/W1971629946","https://openalex.org/W1978842586","https://openalex.org/W2014158063","https://openalex.org/W2014596061","https://openalex.org/W2034399102","https://openalex.org/W2041657594","https://openalex.org/W2046011778","https://openalex.org/W2066090363","https://openalex.org/W2066119858","https://openalex.org/W2069106362","https://openalex.org/W2076705709","https://openalex.org/W2083498017","https://openalex.org/W2085044339","https://openalex.org/W2096925994","https://openalex.org/W2097117768","https://openalex.org/W2097179345","https://openalex.org/W2098411764","https://openalex.org/W2105101328","https://openalex.org/W2105628133","https://openalex.org/W2106253207","https://openalex.org/W2108146080","https://openalex.org/W2108598243","https://openalex.org/W2108995755","https://openalex.org/W2109677803","https://openalex.org/W2112074816","https://openalex.org/W2115733720","https://openalex.org/W2121085248","https://openalex.org/W2131459196","https://openalex.org/W2134270519","https://openalex.org/W2141135812","https://openalex.org/W2147502347","https://openalex.org/W2148603752","https://openalex.org/W2151452918","https://openalex.org/W2153635508","https://openalex.org/W2155541015","https://openalex.org/W2160684493","https://openalex.org/W2168029744","https://openalex.org/W2169073444","https://openalex.org/W2175349549","https://openalex.org/W2183460364","https://openalex.org/W2390960427","https://openalex.org/W2407145437","https://openalex.org/W2467076512","https://openalex.org/W2525806779","https://openalex.org/W2563117427","https://openalex.org/W2618712798","https://openalex.org/W2724616073","https://openalex.org/W2770182587","https://openalex.org/W2905522029","https://openalex.org/W2952529932","https://openalex.org/W3101749733","https://openalex.org/W3143107425","https://openalex.org/W4230674625"],"related_works":["https://openalex.org/W2982181895","https://openalex.org/W2739115385","https://openalex.org/W2035803264","https://openalex.org/W2039527536","https://openalex.org/W3035209767","https://openalex.org/W3007862902","https://openalex.org/W4306891336","https://openalex.org/W1965763148","https://openalex.org/W1966820346","https://openalex.org/W2967794374"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"a":[3,25,39],"novel":[4],"incremental":[5,11,48],"dimensionality":[6],"reduction":[7],"(DR)":[8],"technique":[9],"called":[10],"accelerated":[12],"kernel":[13,37,110],"discriminant":[14],"analysis":[15],"(IAKDA)":[16],"is":[17,43],"proposed.":[18],"Consisting":[19],"of":[20,24,35,54],"the":[21,30,36,64,78],"eigenvalue":[22],"decomposition":[23],"relatively":[26],"small-size":[27],"matrix":[28],"and":[29,74,94,104,109],"recursive":[31],"block":[32],"Cholesky":[33],"factorization":[34,52],"matrix,":[38],"nonlinear":[40],"DR":[41],"transformation":[42],"efficiently":[44],"computed":[45],"at":[46],"each":[47],"step.":[49],"Moreover,":[50],"employing":[51],"techniques":[53],"excellent":[55],"numerical":[56],"stability,":[57],"IAKDA":[58],"effectively":[59],"removes":[60],"data":[61],"nonlinearities":[62],"in":[63],"low":[65],"dimensional":[66],"subspace.":[67],"Experimental":[68],"evaluation":[69],"on":[70],"various":[71],"multimedia":[72],"tasks":[73],"datasets":[75],"confirms":[76],"that":[77],"proposed":[79],"approach":[80],"combined":[81],"with":[82],"linear":[83],"support":[84],"vector":[85],"machines":[86],"(LSVMs)":[87],"offers":[88],"improved":[89],"mean":[90],"average":[91],"precision":[92],"(MAP)":[93],"provides":[95],"an":[96],"impressive":[97],"training":[98],"time":[99],"speedup":[100],"over":[101,106],"batch":[102],"KDA":[103],"also":[105],"traditional":[107],"LSVM":[108],"SVM":[111],"(KSVM).":[112]},"counts_by_year":[{"year":2019,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}