{"id":"https://openalex.org/W4405633680","doi":"https://doi.org/10.1109/mlnlp63328.2024.10800445","title":"Non-negative Matrix Tri-Factorization Based on Anchor Graph for Clustering","display_name":"Non-negative Matrix Tri-Factorization Based on Anchor Graph for Clustering","publication_year":2024,"publication_date":"2024-10-18","ids":{"openalex":"https://openalex.org/W4405633680","doi":"https://doi.org/10.1109/mlnlp63328.2024.10800445"},"language":"en","primary_location":{"id":"doi:10.1109/mlnlp63328.2024.10800445","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlnlp63328.2024.10800445","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP)","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/A5100386226","display_name":"Ying Zhang","orcid":"https://orcid.org/0000-0003-1792-0121"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ying Zhang","raw_affiliation_strings":["School of mathematics and statistics, Xidian University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"School of mathematics and statistics, Xidian University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072019950","display_name":"Shuisheng Zhou","orcid":"https://orcid.org/0000-0003-4764-9483"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuisheng Zhou","raw_affiliation_strings":["School of mathematics and statistics, Xidian University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"School of mathematics and statistics, Xidian University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058330865","display_name":"Tongxin Xu","orcid":"https://orcid.org/0000-0002-5418-3020"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tongxin Xu","raw_affiliation_strings":["School of mathematics and statistics, Xidian University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"School of mathematics and statistics, Xidian University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100386226"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24427643,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.7572000026702881,"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":0.7572000026702881,"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":0.722000002861023,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6117616295814514},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5993996858596802},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5833563208580017},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.4727899730205536},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.44134166836738586},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3511982858181},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26065146923065186},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23922333121299744},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08439654111862183}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6117616295814514},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5993996858596802},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5833563208580017},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.4727899730205536},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.44134166836738586},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3511982858181},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26065146923065186},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23922333121299744},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08439654111862183},{"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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlnlp63328.2024.10800445","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlnlp63328.2024.10800445","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP)","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":20,"referenced_works":["https://openalex.org/W1902027874","https://openalex.org/W1979089718","https://openalex.org/W1993962865","https://openalex.org/W2013912476","https://openalex.org/W2089609272","https://openalex.org/W2108119513","https://openalex.org/W2168103112","https://openalex.org/W2422268042","https://openalex.org/W2569859441","https://openalex.org/W2598052950","https://openalex.org/W2896169497","https://openalex.org/W2964837678","https://openalex.org/W3033138310","https://openalex.org/W3160787088","https://openalex.org/W3176216147","https://openalex.org/W3196984294","https://openalex.org/W4310494058","https://openalex.org/W4392366596","https://openalex.org/W6680012447","https://openalex.org/W6684578312"],"related_works":["https://openalex.org/W2794559785","https://openalex.org/W1754499339","https://openalex.org/W2013873776","https://openalex.org/W4298130764","https://openalex.org/W2804364458","https://openalex.org/W2950281908","https://openalex.org/W2963117165","https://openalex.org/W2084977674","https://openalex.org/W1973739845","https://openalex.org/W119752240"],"abstract_inverted_index":{"The":[0,16,157],"graph-based":[1,17],"clustering":[2,11],"approach":[3,18,41],"is":[4,52,62,89,99,115,162],"a":[5,75],"classical":[6],"method,":[7],"which":[8,51],"transforms":[9],"the":[10,40,84,95,103,107,122,130,160],"problem":[12],"to":[13,43,91,101,117,127,129,153],"graph":[14,112,143],"cut.":[15],"has":[19],"two":[20],"key":[21],"steps:(1)":[22],"construction":[23,113],"of":[24,28,68,159],"graph;":[25],"(2)":[26],"proposing":[27],"graph.":[29],"For":[30],"lager-scale":[31],"data":[32,35,104,123,145],"set":[33],"with":[34],"size":[36],"<tex":[37,47],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[38,48],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$n$</tex>,":[39],"needs":[42],"construct":[44,118],"and":[45,54,94,121,144],"propose":[46,74],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$n^{2}$</tex>":[49],"matrices,":[50],"inefficient":[53],"ineffective.":[55],"To":[56],"this":[57,71,133,155],"end,":[58],"an":[59,110,149],"important":[60],"technique":[61,114],"find":[63],"anchor":[64,81],"bipartite":[65,119,142],"graphs":[66],"instead":[67],"graphs.":[69],"In":[70],"paper,":[72],"we":[73,136],"non-negative":[76],"matrix":[77,87],"tri-factorization":[78],"based":[79],"on":[80,166],"graph,":[82,120],"where":[83],"first":[85],"layer":[86,97],"factorization":[88,98],"employed":[90,116],"search":[92],"anchors,":[93,141],"second":[96],"used":[100],"generate":[102],"representations.":[105,146],"At":[106],"same":[108],"time,":[109],"adaptive":[111],"representations":[124],"are":[125],"required":[126],"conform":[128],"bipartite.":[131],"Under":[132],"unified":[134],"framework,":[135],"can":[137],"simultaneously":[138],"learn":[139],"appropriate":[140],"We":[147],"use":[148],"alternate":[150],"iteration":[151],"algorithm":[152,161],"solve":[154],"optimization.":[156],"effectiveness":[158],"verified":[163],"by":[164],"experiments":[165],"six":[167],"benchmark":[168],"datasets.":[169]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
