{"id":"https://openalex.org/W4415537407","doi":"https://doi.org/10.1145/3746027.3755739","title":"Multi-view Clustering Based on Probabilistic Tensor Regression","display_name":"Multi-view Clustering Based on Probabilistic Tensor Regression","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415537407","doi":"https://doi.org/10.1145/3746027.3755739"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3755739","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755739","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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/A5090711705","display_name":"Yichen Bao","orcid":"https://orcid.org/0009-0008-9456-1971"},"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":"Yichen Bao","raw_affiliation_strings":["Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0009-0008-9456-1971","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Yuxuan Liu","orcid":"https://orcid.org/0009-0002-1627-6039"},"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":"Yuxuan Liu","raw_affiliation_strings":["Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0009-0002-1627-6039","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101816805","display_name":"Yu Duan","orcid":"https://orcid.org/0000-0003-1799-7698"},"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":"Yu Duan","raw_affiliation_strings":["Xidian University, Xi'an, ShannXi, China"],"raw_orcid":"https://orcid.org/0000-0003-1799-7698","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, ShannXi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101832260","display_name":"Jing Li","orcid":"https://orcid.org/0000-0002-9592-6193"},"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":"Jing Li","raw_affiliation_strings":["Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0002-9592-6193","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048220966","display_name":"Quanxue Gao","orcid":"https://orcid.org/0000-0001-5082-2940"},"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":"Quanxue Gao","raw_affiliation_strings":["Xidian University, Xi'an, Shaanxi, China"],"raw_orcid":"https://orcid.org/0000-0001-5082-2940","affiliations":[{"raw_affiliation_string":"Xidian University, Xi'an, Shaanxi, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5090711705"],"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":0.28571429,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2169","last_page":"2177"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12303","display_name":"Tensor decomposition and applications","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9718999862670898,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9577000141143799,"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/cluster-analysis","display_name":"Cluster analysis","score":0.6908000111579895},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6388000249862671},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5860999822616577},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5174000263214111},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5091999769210815},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.44209998846054077},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.42899999022483826},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3508000075817108},{"id":"https://openalex.org/keywords/directed-graph","display_name":"Directed graph","score":0.33009999990463257}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6908000111579895},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6388000249862671},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5860999822616577},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.555400013923645},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5174000263214111},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5091999769210815},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.44209998846054077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4392000138759613},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.42899999022483826},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38909998536109924},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3528999984264374},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.33009999990463257},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30790001153945923},{"id":"https://openalex.org/C49555168","wikidata":"https://www.wikidata.org/wiki/Q176583","display_name":"Stochastic matrix","level":3,"score":0.30630001425743103},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.2761000096797943},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.265500009059906},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.2615000009536743},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3755739","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3755739","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3118748497","display_name":null,"funder_award_id":"62176203","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G707331905","display_name":null,"funder_award_id":"2025JC-QYCX-051","funder_id":"https://openalex.org/F4320336567","funder_display_name":"Natural Science Basic Research Program of Shaanxi Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336567","display_name":"Natural Science Basic Research Program of Shaanxi Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W2043571470","https://openalex.org/W2396833522","https://openalex.org/W2787885212","https://openalex.org/W2921065608","https://openalex.org/W2940933409","https://openalex.org/W2949579163","https://openalex.org/W2978297109","https://openalex.org/W3044063956","https://openalex.org/W3072262295","https://openalex.org/W3117265891","https://openalex.org/W4200226646","https://openalex.org/W4280632002","https://openalex.org/W4283827931","https://openalex.org/W4296438183","https://openalex.org/W4383215492","https://openalex.org/W4383220174","https://openalex.org/W4385976158","https://openalex.org/W4386413460","https://openalex.org/W4387969957","https://openalex.org/W4388002642","https://openalex.org/W4388341833","https://openalex.org/W4389664892","https://openalex.org/W4391653295","https://openalex.org/W4398762581","https://openalex.org/W4399576023","https://openalex.org/W4400315096","https://openalex.org/W4401863240","https://openalex.org/W4402753721","https://openalex.org/W4405386671","https://openalex.org/W4406062812","https://openalex.org/W4408124984"],"related_works":[],"abstract_inverted_index":{"Multi-view":[0],"clustering":[1,50],"based":[2,52],"on":[3,53,136],"anchor":[4,29,66,76,111,138],"graph":[5,67,77,112],"and":[6,16,31,95,127,130,141,153],"regression":[7,56,62,114,117],"is":[8,38],"widely":[9],"used":[10],"to":[11,84,99,115,118,146],"deal":[12],"with":[13],"high":[14],"dimensional":[15],"redundant":[17],"data.":[18],"However,":[19],"most":[20],"of":[21,28,64,71,93,105,150,158,163],"these":[22,44],"methods":[23],"ignore":[24],"the":[25,32,61,65,69,75,79,88,102,110,124,132,137,142,148,151,161],"probabilistic":[26,54],"characteristics":[27],"graph,":[30],"effective":[33],"information":[34,104,121],"in":[35],"different":[36],"views":[37],"not":[39],"fully":[40],"mined.":[41],"To":[42],"solve":[43],"problems,":[45],"we":[46,59,86,108],"propose":[47],"a":[48],"multi-view":[49,106],"method":[51],"tensor":[55,116,140,145],"(MVCPTR).":[57],"Specifically,":[58],"reinterpret":[60],"process":[63],"from":[68,82],"perspective":[70],"probability.":[72],"By":[73],"modeling":[74],"as":[78],"transition":[80],"probability":[81],"samples":[83,94],"anchors,":[85],"construct":[87],"implicit":[89],"relationship":[90],"between":[91],"labels":[92],"anchors.":[96],"In":[97],"order":[98],"further":[100],"mine":[101],"complementary":[103],"data,":[107],"extend":[109],"matrix":[113],"achieve":[119],"multi-level":[120],"fusion":[122],"at":[123],"representational":[125],"level":[126],"decision":[128],"level,":[129],"impose":[131],"Schatten":[133],"p-norm":[134],"constraint":[135],"label":[139,144],"sample":[143],"realize":[147],"bi-clustering":[149],"anchors":[152],"samples.":[154],"A":[155],"large":[156],"number":[157],"experiments":[159],"prove":[160],"effectiveness":[162],"our":[164],"proposed":[165],"algorithm.":[166]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-25T00:00:00"}