{"id":"https://openalex.org/W3125654656","doi":"https://doi.org/10.1109/tnnls.2020.3029033","title":"Semisupervised Feature Learning by Deep Entropy-Sparsity Subspace Clustering","display_name":"Semisupervised Feature Learning by Deep Entropy-Sparsity Subspace Clustering","publication_year":2021,"publication_date":"2021-01-25","ids":{"openalex":"https://openalex.org/W3125654656","doi":"https://doi.org/10.1109/tnnls.2020.3029033","mag":"3125654656","pmid":"https://pubmed.ncbi.nlm.nih.gov/33493120"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2020.3029033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2020.3029033","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Sheng Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sheng Wu","raw_affiliation_strings":["School of Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":null,"display_name":"Wei-Shi Zheng","orcid":"https://orcid.org/0000-0001-8327-0003"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei-Shi Zheng","raw_affiliation_strings":["School of Computer Science, Sun Yat-sen University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-8327-0003","affiliations":[{"raw_affiliation_string":"School of Computer Science, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":1.0364,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.7779475,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"33","issue":"2","first_page":"774","last_page":"788"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.38109999895095825,"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":0.38109999895095825,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.11010000109672546,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.061400000005960464,"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/softmax-function","display_name":"Softmax function","score":0.8287000060081482},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.680899977684021},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6572999954223633},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6373000144958496},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.6103000044822693},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5565000176429749},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.5357000231742859},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5103999972343445}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.8287000060081482},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.814300000667572},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.680899977684021},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6572999954223633},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6568999886512756},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6373000144958496},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.6103000044822693},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5565000176429749},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.5357000231742859},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5103999972343445},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.47369998693466187},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.448199987411499},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.35370001196861267},{"id":"https://openalex.org/C39235581","wikidata":"https://www.wikidata.org/wiki/Q5158434","display_name":"Conceptual clustering","level":5,"score":0.33799999952316284},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2964000105857849},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2020.3029033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2020.3029033","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"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 Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:33493120","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/33493120","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3167315294","display_name":null,"funder_award_id":"U1911401","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5372072380","display_name":null,"funder_award_id":"U1811461","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W370501591","https://openalex.org/W1520997877","https://openalex.org/W1907775068","https://openalex.org/W1931947167","https://openalex.org/W1939575207","https://openalex.org/W1963932623","https://openalex.org/W1975517671","https://openalex.org/W1993962865","https://openalex.org/W1997201895","https://openalex.org/W2011464887","https://openalex.org/W2027922120","https://openalex.org/W2076434944","https://openalex.org/W2096733369","https://openalex.org/W2118120419","https://openalex.org/W2123921160","https://openalex.org/W2144796873","https://openalex.org/W2145038566","https://openalex.org/W2148214025","https://openalex.org/W2159291644","https://openalex.org/W2162262658","https://openalex.org/W2162915993","https://openalex.org/W2177287823","https://openalex.org/W2178768799","https://openalex.org/W2194775991","https://openalex.org/W2199534117","https://openalex.org/W2203392907","https://openalex.org/W2468752595","https://openalex.org/W2500751094","https://openalex.org/W2520774990","https://openalex.org/W2535798196","https://openalex.org/W2564013914","https://openalex.org/W2596763562","https://openalex.org/W2604896999","https://openalex.org/W2608862709","https://openalex.org/W2666784499","https://openalex.org/W2737186053","https://openalex.org/W2741121443","https://openalex.org/W2741998188","https://openalex.org/W2766615582","https://openalex.org/W2792823099","https://openalex.org/W2883492944","https://openalex.org/W2883725317","https://openalex.org/W2897197428","https://openalex.org/W2902373685","https://openalex.org/W2931883679","https://openalex.org/W6603183647","https://openalex.org/W6623329352","https://openalex.org/W6634343353","https://openalex.org/W6675751002","https://openalex.org/W6681239517","https://openalex.org/W6682250724","https://openalex.org/W6684578312","https://openalex.org/W6714590955","https://openalex.org/W6717772578","https://openalex.org/W6718379498","https://openalex.org/W6726873649","https://openalex.org/W6729265172","https://openalex.org/W6729665116","https://openalex.org/W6729767818","https://openalex.org/W6732380306","https://openalex.org/W6738597727","https://openalex.org/W6745910872","https://openalex.org/W6747899497","https://openalex.org/W6764051988","https://openalex.org/W6787972765"],"related_works":[],"abstract_inverted_index":{"While":[0],"feature":[1,41,51,93],"learning":[2,42,47,52,94],"by":[3,53,81,99],"deep":[4,39,61,71,91,108,134,145],"neural":[5,72],"networks":[6],"is":[7,12,148],"currently":[8],"widely":[9],"used,":[10],"it":[11],"still":[13],"very":[14,22],"challenging":[15],"to":[16,34,43,49,74,116],"perform":[17],"this":[18,30],"task,":[19],"given":[20],"the":[21,100,107,118,132,152],"limited":[23],"quantity":[24],"of":[25],"labeled":[26],"data.":[27],"To":[28,105],"solve":[29],"problem,":[31],"we":[32,58,111],"propose":[33],"unite":[35],"subspace":[36,54,63,78,96],"clustering":[37,64,79,97],"with":[38,139],"semisupervised":[40,92],"form":[44],"a":[45,60,70,126],"unified":[46],"framework":[48],"pursue":[50],"clustering.":[55],"More":[56],"specifically,":[57],"develop":[59],"entropy-sparsity":[62,84],"(deep":[65],"ESSC)":[66],"model,":[67,110],"which":[68],"forces":[69],"network":[73],"learn":[75],"features":[76],"using":[77],"constrained":[80],"our":[82,144],"designed":[83],"scheme.":[85],"The":[86],"model":[87,147],"can":[88],"inherently":[89],"harmonize":[90],"and":[95],"simultaneously":[98],"proposed":[101,133],"self-similarity":[102],"preserving":[103],"strategy.":[104],"optimize":[106],"ESSC":[109,135,146],"introduce":[112],"two":[113,119],"unconstrained":[114],"variables":[115],"eliminate":[117],"constraints":[120],"via":[121],"softmax":[122],"functions.":[123],"We":[124],"provide":[125],"general":[127],"algebraic-treatment":[128],"scheme":[129],"for":[130],"solving":[131],"model.":[136],"Extensive":[137],"experiments":[138],"comprehensive":[140],"analysis":[141],"substantiate":[142],"that":[143],"more":[149],"effective":[150],"than":[151],"related":[153],"methods.":[154]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2021-02-01T00:00:00"}