{"id":"https://openalex.org/W4403780758","doi":"https://doi.org/10.1145/3664647.3681223","title":"Scalable Multi-view Unsupervised Feature Selection with Structure Learning and Fusion","display_name":"Scalable Multi-view Unsupervised Feature Selection with Structure Learning and Fusion","publication_year":2024,"publication_date":"2024-10-26","ids":{"openalex":"https://openalex.org/W4403780758","doi":"https://doi.org/10.1145/3664647.3681223"},"language":"en","primary_location":{"id":"doi:10.1145/3664647.3681223","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681223","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"conference-paper","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/A5050092103","display_name":"Chenglong Zhang","orcid":"https://orcid.org/0009-0003-6710-3805"},"institutions":[{"id":"https://openalex.org/I163151501","display_name":"Hangzhou Normal University","ror":"https://ror.org/014v1mr15","country_code":"CN","type":"education","lineage":["https://openalex.org/I163151501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglong Zhang","raw_affiliation_strings":["Hangzhou Normal University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0009-0003-6710-3805","affiliations":[{"raw_affiliation_string":"Hangzhou Normal University, Hangzhou, China","institution_ids":["https://openalex.org/I163151501"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076391595","display_name":"Xinyan Liang","orcid":"https://orcid.org/0000-0003-2589-5392"},"institutions":[{"id":"https://openalex.org/I181877577","display_name":"Shanxi University","ror":"https://ror.org/03y3e3s17","country_code":"CN","type":"education","lineage":["https://openalex.org/I181877577"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyan Liang","raw_affiliation_strings":["Shanxi University, Taiyuan, China"],"raw_orcid":"https://orcid.org/0000-0003-2589-5392","affiliations":[{"raw_affiliation_string":"Shanxi University, Taiyuan, China","institution_ids":["https://openalex.org/I181877577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004080941","display_name":"Peng Zhou","orcid":"https://orcid.org/0000-0002-3675-4985"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Zhou","raw_affiliation_strings":["Anhui University, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0002-3675-4985","affiliations":[{"raw_affiliation_string":"Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088982563","display_name":"Zhaolong Ling","orcid":"https://orcid.org/0000-0003-4812-6676"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaolong Ling","raw_affiliation_strings":["Anhui University, Hefei, China"],"raw_orcid":"https://orcid.org/0000-0003-4812-6676","affiliations":[{"raw_affiliation_string":"Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101808278","display_name":"Yingwei Zhang","orcid":"https://orcid.org/0000-0002-6582-1745"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingwei Zhang","raw_affiliation_strings":["Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6582-1745","affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101768802","display_name":"Xingyu Wu","orcid":"https://orcid.org/0000-0002-8204-6197"},"institutions":[{"id":"https://openalex.org/I14243506","display_name":"Hong Kong Polytechnic University","ror":"https://ror.org/0030zas98","country_code":"HK","type":"education","lineage":["https://openalex.org/I14243506"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Xingyu Wu","raw_affiliation_strings":["Hong Kong Polytechnic University, Hong Kong SAR, China"],"raw_orcid":"https://orcid.org/0000-0002-8204-6197","affiliations":[{"raw_affiliation_string":"Hong Kong Polytechnic University, Hong Kong SAR, China","institution_ids":["https://openalex.org/I14243506"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019915591","display_name":"Weiguo Sheng","orcid":"https://orcid.org/0000-0001-9680-5126"},"institutions":[{"id":"https://openalex.org/I163151501","display_name":"Hangzhou Normal University","ror":"https://ror.org/014v1mr15","country_code":"CN","type":"education","lineage":["https://openalex.org/I163151501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiguo Sheng","raw_affiliation_strings":["Hangzhou Normal University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9680-5126","affiliations":[{"raw_affiliation_string":"Hangzhou Normal University, Hangzhou, China","institution_ids":["https://openalex.org/I163151501"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5013029508","display_name":"Bingbing Jiang","orcid":"https://orcid.org/0000-0003-2217-6202"},"institutions":[{"id":"https://openalex.org/I163151501","display_name":"Hangzhou Normal University","ror":"https://ror.org/014v1mr15","country_code":"CN","type":"education","lineage":["https://openalex.org/I163151501"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bingbing Jiang","raw_affiliation_strings":["Hangzhou Normal University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2217-6202","affiliations":[{"raw_affiliation_string":"Hangzhou Normal University, Hangzhou, China","institution_ids":["https://openalex.org/I163151501"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5479","last_page":"5488"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987999796867371,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987999796867371,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9983000159263611,"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/T10057","display_name":"Face and Expression Recognition","score":0.9979000091552734,"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/computer-science","display_name":"Computer science","score":0.7848296761512756},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6819177269935608},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6202321648597717},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5589639544487},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5346146821975708},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5045217275619507},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.4976673424243927},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4586181640625},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4489273428916931},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3358945846557617},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.0648793876171112}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7848296761512756},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6819177269935608},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6202321648597717},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5589639544487},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5346146821975708},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5045217275619507},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.4976673424243927},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4586181640625},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4489273428916931},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3358945846557617},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0648793876171112},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3664647.3681223","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3664647.3681223","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 32nd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W1644402181","https://openalex.org/W2187089797","https://openalex.org/W2210977594","https://openalex.org/W2422268042","https://openalex.org/W2583916359","https://openalex.org/W2595272553","https://openalex.org/W2602753196","https://openalex.org/W2808219330","https://openalex.org/W2903665248","https://openalex.org/W2904125861","https://openalex.org/W2911627187","https://openalex.org/W2965510190","https://openalex.org/W2996966849","https://openalex.org/W3000308450","https://openalex.org/W3114137134","https://openalex.org/W3115527261","https://openalex.org/W3117298090","https://openalex.org/W3120213044","https://openalex.org/W3207537336","https://openalex.org/W3212266429","https://openalex.org/W3213291467","https://openalex.org/W4210518188","https://openalex.org/W4220733437","https://openalex.org/W4225942313","https://openalex.org/W4288064731","https://openalex.org/W4290716501","https://openalex.org/W4290998933","https://openalex.org/W4304091655","https://openalex.org/W4304091814","https://openalex.org/W4304481487","https://openalex.org/W4307501835","https://openalex.org/W4308146710","https://openalex.org/W4315473677","https://openalex.org/W4323345705","https://openalex.org/W4323545792","https://openalex.org/W4367849720","https://openalex.org/W4377079741","https://openalex.org/W4377079796","https://openalex.org/W4379208636","https://openalex.org/W4383533011","https://openalex.org/W4383677217","https://openalex.org/W4386233471","https://openalex.org/W4386395835","https://openalex.org/W4386600514","https://openalex.org/W4386952124","https://openalex.org/W4387968203","https://openalex.org/W4392452744","https://openalex.org/W4392745198","https://openalex.org/W4393156830","https://openalex.org/W4393160284","https://openalex.org/W4393380945","https://openalex.org/W4401024794"],"related_works":["https://openalex.org/W2389214306","https://openalex.org/W4235240664","https://openalex.org/W3174759195","https://openalex.org/W3167013339","https://openalex.org/W4287121366","https://openalex.org/W60493759","https://openalex.org/W4308619659","https://openalex.org/W3213069564","https://openalex.org/W4378421684","https://openalex.org/W4294203825"],"abstract_inverted_index":{"To":[0,82],"tackle":[1],"the":[2,24,32,36,43,47,58,63,66,72,102,106,114,120,130,156,160,191,199],"high-dimensional":[3],"data":[4],"with":[5,91],"multiple":[6],"representations,":[7],"multi-view":[8],"unsupervised":[9],"feature":[10,148],"selection":[11],"has":[12,174],"emerged":[13],"as":[14,129],"a":[15,85,136],"significant":[16],"learning":[17,93,154],"paradigm.":[18],"However,":[19],"previous":[20],"methods":[21],"suffer":[22],"from":[23,77,155],"following":[25],"dilemmas:":[26],"(i)":[27],"They":[28],"focus":[29],"on":[30,57],"selecting":[31],"features":[33],"that":[34,123,135],"preserve":[35],"similarity":[37,73,107],"structure":[38,76,92,104],"of":[39,109,162,201],"data,":[40],"whereas":[41],"neglecting":[42],"discriminative":[44,168],"information":[45],"in":[46,65],"cluster":[48,60,67,75,103,125,132,163],"structure;":[49],"(ii)":[50],"The":[51],"orthogonal":[52],"constraint":[53],"is":[54,80,97,187],"often":[55],"imposed":[56],"pseudo":[59,131],"labels,":[61,133],"breaking":[62],"locality":[64,161],"label":[68],"space;":[69],"(iii)":[70],"Learning":[71],"or":[74],"all":[78],"samples":[79],"time-consuming.":[81],"this":[83],"end,":[84],"Scalable":[86],"Multi-view":[87],"Unsupervised":[88],"Feature":[89],"Selection":[90],"and":[94,105,127,165,195],"fusion":[95],"(SMUFS)":[96],"proposed":[98],"to":[99,117,146,177,189],"jointly":[100],"exploit":[101],"relations":[108],"data.":[110,183],"Specifically,":[111],"SMUFS":[112,151,179],"introduces":[113],"sample-view":[115],"weights":[116],"adaptively":[118],"fuse":[119],"membership":[121,138,157],"matrices":[122],"indicate":[124],"structures":[126],"serve":[128],"such":[134],"unified":[137],"matrix":[139],"across":[140],"views":[141],"can":[142],"be":[143],"effectively":[144],"obtained":[145],"guide":[147],"selection.":[149],"Meanwhile,":[150],"performs":[152],"graph":[153],"matrix,":[158],"preserving":[159],"labels":[164],"improving":[166],"their":[167],"capability.":[169],"Further,":[170],"an":[171],"acceleration":[172],"strategy":[173],"been":[175],"developed":[176],"make":[178],"scalable":[180],"for":[181],"large-scale":[182],"An":[184],"iterative":[185],"optimization":[186],"designed":[188],"solve":[190],"formulated":[192],"objective":[193],"function,":[194],"extensive":[196],"experiments":[197],"demonstrate":[198],"superiority":[200],"SMUFS.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}