{"id":"https://openalex.org/W2735772787","doi":"https://doi.org/10.1109/ijcnn.2017.7966327","title":"Non-local information for a mixture of multiple linear classifiers","display_name":"Non-local information for a mixture of multiple linear classifiers","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2735772787","doi":"https://doi.org/10.1109/ijcnn.2017.7966327","mag":"2735772787"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7966327","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","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/A5017086786","display_name":"Weite Li","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Weite Li","raw_affiliation_strings":["Graduate School of Information, Product and Systems, Waseda University, Kitakyushu-shi, Fukuoka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information, Product and Systems, Waseda University, Kitakyushu-shi, Fukuoka, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103260670","display_name":"Peifeng Liang","orcid":"https://orcid.org/0000-0003-1052-3369"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Peifeng Liang","raw_affiliation_strings":["Graduate School of Information, Product and Systems, Waseda University, Kitakyushu-shi, Fukuoka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information, Product and Systems, Waseda University, Kitakyushu-shi, Fukuoka, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100776798","display_name":"Xin Yuan","orcid":"https://orcid.org/0000-0003-3140-3243"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xin Yuan","raw_affiliation_strings":["Graduate School of Information, Product and Systems, Waseda University, Kitakyushu-shi, Fukuoka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information, Product and Systems, Waseda University, Kitakyushu-shi, Fukuoka, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100326923","display_name":"Jinglu Hu","orcid":"https://orcid.org/0000-0002-5601-7261"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jinglu Hu","raw_affiliation_strings":["Graduate School of Information, Product and Systems, Waseda University, Kitakyushu-shi, Fukuoka, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information, Product and Systems, Waseda University, Kitakyushu-shi, Fukuoka, Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150744194"],"apc_list":null,"apc_paid":null,"fwci":0.7389,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.80679079,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":"14","issue":null,"first_page":"3741","last_page":"3746"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9997000098228455,"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":0.9997000098228455,"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/T12676","display_name":"Machine Learning and ELM","score":0.9990000128746033,"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/T10320","display_name":"Neural Networks and Applications","score":0.9976999759674072,"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/computer-science","display_name":"Computer science","score":0.6812334060668945},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.6761146783828735},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5955724716186523},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5876674652099609},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.586410641670227},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5675675272941589},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5410366058349609},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5194239616394043},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.502131462097168},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.47998663783073425},{"id":"https://openalex.org/keywords/kernel-method","display_name":"Kernel method","score":0.47123751044273376},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.4633064866065979},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.45497652888298035},{"id":"https://openalex.org/keywords/divide-and-conquer-algorithms","display_name":"Divide and conquer algorithms","score":0.4480946660041809},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43655097484588623},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3542149066925049},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2327376902103424},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20336416363716125},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.19744068384170532}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6812334060668945},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.6761146783828735},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5955724716186523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5876674652099609},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.586410641670227},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5675675272941589},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5410366058349609},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5194239616394043},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.502131462097168},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.47998663783073425},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.47123751044273376},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.4633064866065979},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.45497652888298035},{"id":"https://openalex.org/C71559656","wikidata":"https://www.wikidata.org/wiki/Q671298","display_name":"Divide and conquer algorithms","level":2,"score":0.4480946660041809},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43655097484588623},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3542149066925049},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2327376902103424},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20336416363716125},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.19744068384170532},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","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/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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2017.7966327","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7966327","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","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/W1522301498","https://openalex.org/W1531103298","https://openalex.org/W1581406946","https://openalex.org/W1676661688","https://openalex.org/W1960174506","https://openalex.org/W1969557815","https://openalex.org/W2054678839","https://openalex.org/W2112796928","https://openalex.org/W2118712128","https://openalex.org/W2145406111","https://openalex.org/W2153635508","https://openalex.org/W2155440340","https://openalex.org/W2156387975","https://openalex.org/W2165401139","https://openalex.org/W2165828254","https://openalex.org/W2189508540","https://openalex.org/W2621615146","https://openalex.org/W3118608800","https://openalex.org/W3120740533","https://openalex.org/W6631708475","https://openalex.org/W6634563840","https://openalex.org/W6637406579","https://openalex.org/W6677758222","https://openalex.org/W6682889407","https://openalex.org/W6683985686","https://openalex.org/W6687078384","https://openalex.org/W6738988723"],"related_works":["https://openalex.org/W3100286349","https://openalex.org/W2896134808","https://openalex.org/W4289378085","https://openalex.org/W4294291164","https://openalex.org/W3172436493","https://openalex.org/W1887135636","https://openalex.org/W4287164812","https://openalex.org/W2386063599","https://openalex.org/W1975884855","https://openalex.org/W3213150849"],"abstract_inverted_index":{"For":[0],"many":[1],"problems":[2],"in":[3,94,109],"machine":[4,26,119],"learning":[5],"fields,":[6],"the":[7,63,86],"data":[8,20,51,56,70,87],"are":[9,82],"nonlinearly":[10],"distributed.":[11],"One":[12],"popular":[13],"way":[14],"to":[15,53,84,90,123],"tackle":[16],"this":[17,59],"kind":[18],"of":[19,30,37,49,75,134,152],"is":[21,66,145],"training":[22,102],"a":[23,28,76,98,103,110,132],"local":[24,43],"kernel":[25],"or":[27,144],"mixture":[29,133],"several":[31,79],"locally":[32,153],"linear":[33,104,136,154],"models.":[34,155],"However,":[35],"both":[36],"these":[38],"approaches":[39],"heavily":[40],"relies":[41],"on":[42],"information,":[44],"such":[45],"as":[46],"neighbor":[47],"relations":[48],"each":[50,92,107],"sample,":[52],"capture":[54],"potential":[55],"distribution.":[57],"In":[58],"paper,":[60],"we":[61],"show":[62],"non-local":[64,80,139],"information":[65,140],"more":[67],"efficient":[68],"for":[69,106],"representation.":[71],"With":[72],"an":[73],"implementation":[74],"winner-take-all":[77],"autoencoder,":[78],"templates":[81],"trained":[83],"trace":[85],"distribution":[88],"and":[89,112],"represent":[91],"sample":[93],"different":[95],"subspaces":[96],"with":[97,149],"suitable":[99],"weight.":[100],"By":[101],"model":[105],"subspace":[108],"divide":[111],"conquer":[113],"manner,":[114],"one":[115],"single":[116],"support":[117],"vector":[118],"can":[120],"be":[121],"formulated":[122],"solve":[124],"nonlinear":[125],"classification":[126],"problems.":[127],"Experimental":[128],"results":[129],"demonstrate":[130],"that":[131],"multiple":[135],"classifiers":[137],"from":[138],"performs":[141],"better":[142],"than":[143],"at":[146],"least":[147],"competitive":[148],"state-of-the-art":[150],"mixtures":[151]},"counts_by_year":[{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":4}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}