{"id":"https://openalex.org/W3199679192","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533825","title":"Spectral Clustering Joint Deep Embedding Learning by Autoencoder","display_name":"Spectral Clustering Joint Deep Embedding Learning by Autoencoder","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3199679192","doi":"https://doi.org/10.1109/ijcnn52387.2021.9533825","mag":"3199679192"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9533825","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533825","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 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/A5063256241","display_name":"Xiucai Ye","orcid":"https://orcid.org/0000-0002-5547-3919"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Xiucai Ye","raw_affiliation_strings":["University of Tsukuba, Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"University of Tsukuba, Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012791383","display_name":"Chunhao Wang","orcid":"https://orcid.org/0000-0002-6945-7119"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Chunhao Wang","raw_affiliation_strings":["University of Tsukuba, Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"University of Tsukuba, Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047435295","display_name":"Akira Imakura","orcid":"https://orcid.org/0000-0003-4994-2499"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akira Imakura","raw_affiliation_strings":["University of Tsukuba, Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"University of Tsukuba, Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038336830","display_name":"Tetsuya Sakurai","orcid":"https://orcid.org/0000-0002-5789-7547"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tetsuya Sakurai","raw_affiliation_strings":["University of Tsukuba, Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"University of Tsukuba, Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5063256241"],"corresponding_institution_ids":["https://openalex.org/I146399215"],"apc_list":null,"apc_paid":null,"fwci":0.2882,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.55866013,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9970999956130981,"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":0.9970999956130981,"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/T13731","display_name":"Advanced Computing and Algorithms","score":0.9908999800682068,"subfield":{"id":"https://openalex.org/subfields/3322","display_name":"Urban Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8726130723953247},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.8165234327316284},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7571980357170105},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7172480821609497},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6887056231498718},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6777760982513428},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.58516526222229},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.5172680020332336},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4894939661026001},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.46051421761512756},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.4371381998062134},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3859047293663025},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3246055841445923}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8726130723953247},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.8165234327316284},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7571980357170105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7172480821609497},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6887056231498718},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6777760982513428},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.58516526222229},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.5172680020332336},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4894939661026001},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.46051421761512756},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.4371381998062134},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3859047293663025},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3246055841445923},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9533825","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9533825","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321034","display_name":"New Energy and Industrial Technology Development Organization","ror":"https://ror.org/0055k7a87"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":50,"referenced_works":["https://openalex.org/W191001584","https://openalex.org/W1555148682","https://openalex.org/W1666782210","https://openalex.org/W1966297316","https://openalex.org/W1966461475","https://openalex.org/W1986747608","https://openalex.org/W1992419399","https://openalex.org/W2051549110","https://openalex.org/W2102409316","https://openalex.org/W2107109500","https://openalex.org/W2109571218","https://openalex.org/W2114508388","https://openalex.org/W2116810533","https://openalex.org/W2121947440","https://openalex.org/W2122538988","https://openalex.org/W2127823872","https://openalex.org/W2132914434","https://openalex.org/W2141465109","https://openalex.org/W2145094598","https://openalex.org/W2152322845","https://openalex.org/W2154642173","https://openalex.org/W2163939328","https://openalex.org/W2165874743","https://openalex.org/W2187089797","https://openalex.org/W2222512263","https://openalex.org/W2266950115","https://openalex.org/W2342032814","https://openalex.org/W2405933695","https://openalex.org/W2741943936","https://openalex.org/W2750384547","https://openalex.org/W2810432309","https://openalex.org/W2903738625","https://openalex.org/W2963399222","https://openalex.org/W2963656735","https://openalex.org/W2964043796","https://openalex.org/W2964074409","https://openalex.org/W2997574889","https://openalex.org/W3003209024","https://openalex.org/W3040359118","https://openalex.org/W3081135305","https://openalex.org/W3089607999","https://openalex.org/W6637138250","https://openalex.org/W6675401909","https://openalex.org/W6676065935","https://openalex.org/W6680735885","https://openalex.org/W6681096077","https://openalex.org/W6684578312","https://openalex.org/W6685380521","https://openalex.org/W6692846177","https://openalex.org/W6735013348"],"related_works":["https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4389832810","https://openalex.org/W2985124601","https://openalex.org/W3205251672","https://openalex.org/W2272768744"],"abstract_inverted_index":{"Spectral":[0,89],"clustering":[1,9,20,27,60,109,141,154,161],"has":[2],"become":[3],"one":[4],"of":[5,25,39,77,107,175],"the":[6,18,23,56,65,69,74,100,104,136,173,176],"most":[7],"popular":[8],"methods":[10,50,71],"due":[11],"to":[12,17,54,64,120,139,158],"its":[13],"superior":[14],"performance":[15,24],"compared":[16],"traditional":[19],"methods.":[21],"However,":[22],"spectral":[26,59,82,105,108,123,126,137,150,153],"would":[28],"be":[29],"limited":[30],"by":[31,155],"complex":[32],"data,":[33],"such":[34],"as":[35],"a":[36,95,111,115,148],"huge":[37],"number":[38],"samples":[40],"and":[41,81,103,169],"high":[42],"dimensionality.":[43],"To":[44],"address":[45],"this":[46,85],"problem,":[47],"some":[48],"existing":[49,70],"apply":[51],"deep":[52,112,156],"learning":[53,80,157],"learn":[55,147],"lower-dimensional":[57],"representations,":[58],"is":[61,118,133],"then":[62],"applied":[63],"representations.":[66],"Different":[67],"from":[68],"that":[72,97],"separate":[73],"two":[75],"stages":[76],"feature":[78,101],"representation":[79],"clustering,":[83],"in":[84],"paper,":[86],"we":[87],"propose":[88],"Clustering":[90],"Joint":[91],"Deep":[92],"Embedding":[93],"(SCJDE),":[94],"method":[96,145],"simultaneously":[98],"learns":[99],"representations":[102],"embedding":[106,124,138,151],"via":[110],"autoencoder.":[113],"Moreover,":[114],"sparsity":[116],"constraint":[117],"imposed":[119],"generate":[121],"better":[122,160],"for":[125,152],"clustering.":[127],"Finally,":[128],"<tex":[129],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[130],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$k$</tex>":[131],"-means":[132],"performed":[134],"on":[135,166],"obtain":[140,159],"result.":[142],"The":[143,163],"proposed":[144,177],"can":[146],"good":[149],"results.":[162],"experimental":[164],"results":[165],"both":[167],"synthetic":[168],"real-world":[170],"datasets":[171],"demonstrate":[172],"effectiveness":[174],"method.":[178]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}