{"id":"https://openalex.org/W2922327873","doi":"https://doi.org/10.23919/apsipa.2018.8659572","title":"Discriminative sparse representation learning using multiclass hinge loss","display_name":"Discriminative sparse representation learning using multiclass hinge loss","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2922327873","doi":"https://doi.org/10.23919/apsipa.2018.8659572","mag":"2922327873"},"language":"en","primary_location":{"id":"doi:10.23919/apsipa.2018.8659572","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5030118693","display_name":"Ryota Kamiya","orcid":null},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Ryota Kamiya","raw_affiliation_strings":["The University of Electro-Communications, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Electro-Communications, Tokyo, Japan","institution_ids":["https://openalex.org/I20529979"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053654332","display_name":"Yoshikazu Washizawa","orcid":"https://orcid.org/0000-0001-8257-518X"},"institutions":[{"id":"https://openalex.org/I20529979","display_name":"University of Electro-Communications","ror":"https://ror.org/02x73b849","country_code":"JP","type":"education","lineage":["https://openalex.org/I20529979"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yoshikazu Washizawa","raw_affiliation_strings":["The University of Electro-Communications, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Electro-Communications, Tokyo, Japan","institution_ids":["https://openalex.org/I20529979"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5030118693"],"corresponding_institution_ids":["https://openalex.org/I20529979"],"apc_list":null,"apc_paid":null,"fwci":0.2027,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54100423,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"955","last_page":"958"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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/T12676","display_name":"Machine Learning and ELM","score":0.9958000183105469,"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/T10057","display_name":"Face and Expression Recognition","score":0.9919999837875366,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hinge-loss","display_name":"Hinge loss","score":0.8884872198104858},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.8529730439186096},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.765291690826416},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.672581672668457},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6718043088912964},{"id":"https://openalex.org/keywords/k-svd","display_name":"K-SVD","score":0.6504911184310913},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6178387403488159},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6143996715545654},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4816107451915741},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4659472703933716},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.4623471796512604},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4278794527053833}],"concepts":[{"id":"https://openalex.org/C39891107","wikidata":"https://www.wikidata.org/wiki/Q5767098","display_name":"Hinge loss","level":3,"score":0.8884872198104858},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.8529730439186096},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.765291690826416},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.672581672668457},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6718043088912964},{"id":"https://openalex.org/C154771677","wikidata":"https://www.wikidata.org/wiki/Q17098361","display_name":"K-SVD","level":3,"score":0.6504911184310913},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6178387403488159},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6143996715545654},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4816107451915741},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4659472703933716},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.4623471796512604},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4278794527053833},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/apsipa.2018.8659572","is_oa":false,"landing_page_url":"https://doi.org/10.23919/apsipa.2018.8659572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1963932623","https://openalex.org/W1986931325","https://openalex.org/W2027805700","https://openalex.org/W2030754587","https://openalex.org/W2084716923","https://openalex.org/W2118585731","https://openalex.org/W2128659236","https://openalex.org/W2157791002","https://openalex.org/W2160547390","https://openalex.org/W2296616510","https://openalex.org/W2315814222","https://openalex.org/W2524438386","https://openalex.org/W3004533406","https://openalex.org/W4235713725","https://openalex.org/W4250955649","https://openalex.org/W6677656871","https://openalex.org/W6679470320","https://openalex.org/W6682953061"],"related_works":["https://openalex.org/W1971373099","https://openalex.org/W2099321050","https://openalex.org/W2890952311","https://openalex.org/W2509955295","https://openalex.org/W2047275718","https://openalex.org/W2034957211","https://openalex.org/W110819671","https://openalex.org/W2113038107","https://openalex.org/W2532569109","https://openalex.org/W2922327873"],"abstract_inverted_index":{"Sparse":[0,10],"representation":[1,11,17,32,41,131,143],"methods":[2],"have":[3],"been":[4],"researched":[5],"widely":[6],"in":[7,97],"recent":[8],"years.":[9],"classification":[12,27,144],"methods,":[13],"such":[14],"as":[15],"sparse":[16,31,40,130,142],"classifier":[18],"(SRC)":[19],"and":[20,30,101,129],"label-consistent":[21],"K-SVD":[22],"(singular":[23],"value":[24],"decomposition)":[25],"learn":[26],"parameters,":[28],"dictionary,":[29],"simultaneously,":[33],"so":[34],"that":[35,120,136],"they":[36],"find":[37],"an":[38,112],"optimal":[39,65],"to":[42,54,88,115],"discriminate":[43],"categories.":[44],"However,":[45],"these":[46],"classifiers":[47,67],"use":[48],"least":[49],"square":[50],"error":[51],"(LSE)":[52],"strategy":[53],"design":[55,89],"the":[56,60,79,85,90,117,122,126,137],"classifiers.":[57],"LSE":[58,107],"of":[59,125],"empirical":[61,80],"risk":[62],"is":[63,95,121],"not":[64],"for":[66],"because":[68],"even":[69],"if":[70],"a":[71],"training":[72],"sample":[73],"correctly":[74],"classified,":[75],"it":[76,102],"may":[77],"increase":[78],"cost.":[81],"We,":[82],"therefore,":[83],"introduce":[84],"hinge":[86,93,127],"loss":[87,94,128],"classifier.":[91],"The":[92],"employed":[96],"support":[98],"vector":[99],"machines,":[100],"shows":[103],"better":[104],"performance":[105],"than":[106],"based":[108],"methods.":[109,145],"We":[110],"provide":[111],"optimization":[113],"algorithm":[114],"minimize":[116],"proposed":[118,138],"criterion":[119],"linear":[123],"combination":[124],"error.":[132],"Experimental":[133],"results":[134],"show":[135],"method":[139],"exhibited":[140],"conventional":[141]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
