{"id":"https://openalex.org/W4296437520","doi":"https://doi.org/10.1109/access.2022.3207930","title":"Unsupervised Feature Selection via Metric Fusion and Novel Low-Rank Approximation","display_name":"Unsupervised Feature Selection via Metric Fusion and Novel Low-Rank Approximation","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4296437520","doi":"https://doi.org/10.1109/access.2022.3207930"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3207930","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3207930","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2022.3207930","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014017132","display_name":"Yin Long","orcid":"https://orcid.org/0000-0001-9714-5350"},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yin Long","raw_affiliation_strings":["School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China","institution_ids":["https://openalex.org/I1297991670"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100334649","display_name":"Liang Chen","orcid":"https://orcid.org/0000-0003-1453-1091"},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Chen","raw_affiliation_strings":["School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China","institution_ids":["https://openalex.org/I1297991670"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100343206","display_name":"Linfeng Li","orcid":null},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linfeng Li","raw_affiliation_strings":["School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China","institution_ids":["https://openalex.org/I1297991670"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042116683","display_name":"Rong Shi","orcid":"https://orcid.org/0000-0002-5152-3026"},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Shi","raw_affiliation_strings":["School of Applied Technology, Southwest University of Science and Technology, Mianyang, China"],"affiliations":[{"raw_affiliation_string":"School of Applied Technology, Southwest University of Science and Technology, Mianyang, China","institution_ids":["https://openalex.org/I1297991670"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5014017132"],"corresponding_institution_ids":["https://openalex.org/I1297991670"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08598621,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":null,"first_page":"101474","last_page":"101482"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","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/T10057","display_name":"Face and Expression Recognition","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9958999752998352,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/computer-science","display_name":"Computer science","score":0.6035140752792358},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5700123310089111},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5656315088272095},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5368888974189758},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5345607995986938},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4738757312297821},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47130095958709717},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.45512598752975464},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.35395491123199463},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32334983348846436}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6035140752792358},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5700123310089111},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5656315088272095},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5368888974189758},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5345607995986938},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4738757312297821},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47130095958709717},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.45512598752975464},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.35395491123199463},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32334983348846436},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3207930","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3207930","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:76fa65dfca4b4bba9d259d372d32d18a","is_oa":true,"landing_page_url":"https://doaj.org/article/76fa65dfca4b4bba9d259d372d32d18a","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 101474-101482 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3207930","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3207930","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4200988844","display_name":null,"funder_award_id":"62101467","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W19235576","https://openalex.org/W1963763787","https://openalex.org/W2076363162","https://openalex.org/W2083666679","https://openalex.org/W2100659887","https://openalex.org/W2121410881","https://openalex.org/W2121647436","https://openalex.org/W2137758646","https://openalex.org/W2153922613","https://openalex.org/W2159400887","https://openalex.org/W2422268042","https://openalex.org/W2505029951","https://openalex.org/W2588939073","https://openalex.org/W2748391982","https://openalex.org/W2765158981","https://openalex.org/W2788471974","https://openalex.org/W3016633245","https://openalex.org/W3040349040","https://openalex.org/W3087373232","https://openalex.org/W3102376457","https://openalex.org/W4205479679","https://openalex.org/W6682644385"],"related_works":["https://openalex.org/W2770593030","https://openalex.org/W3154990682","https://openalex.org/W2560201613","https://openalex.org/W2171975302","https://openalex.org/W2022352247","https://openalex.org/W2488129135","https://openalex.org/W4312219546","https://openalex.org/W2787993192","https://openalex.org/W2377538627","https://openalex.org/W2107220315"],"abstract_inverted_index":{"Unsupervised":[0],"feature":[1,33,72],"selection":[2,34],"aims":[3],"to":[4,25,37,64,101,106,124],"derive":[5],"a":[6,60,113],"compact":[7],"set":[8],"of":[9,28,52,152],"features":[10,46],"with":[11,168],"desired":[12],"generalization":[13],"ability":[14],"via":[15],"removing":[16],"the":[17,26,39,44,50,79,102,108,125,129,138,149,153],"irrelevant":[18],"and":[19,76,140],"redundant":[20],"features,":[21],"yet":[22],"challenging":[23],"due":[24],"unavailability":[27],"labels.":[29],"Works":[30],"about":[31],"unsupervised":[32],"always":[35],"need":[36],"construct":[38],"similarity":[40,53,66,96],"matrix,":[41,67],"which":[42,68,120],"makes":[43],"selected":[45],"highly":[47],"depend":[48],"on":[49],"accuracy":[51,150],"measurement.":[54],"However,":[55],"existing":[56],"works":[57],"usually":[58],"leverage":[59],"single":[61],"fixed":[62],"metric":[63],"build":[65],"cannot":[69],"fit":[70],"various":[71],"types":[73],"very":[74],"well":[75],"even":[77],"damage":[78],"local":[80],"manifold":[81],"structure.":[82],"To":[83],"address":[84],"this":[85],"problem,":[86],"we":[87],"propose":[88],"an":[89],"adaptive":[90],"multi-metric":[91],"fusion":[92],"by":[93,159],"automatically":[94],"integrating":[95],"across":[97],"different":[98],"metrics":[99],"according":[100],"specific":[103],"data.":[104],"Besides,":[105],"capture":[107],"global":[109],"structure":[110],"more":[111],"precisely,":[112],"novel":[114,131],"low-rank":[115,132],"approximation":[116,133],"method":[117,155],"is":[118,121],"proposed,":[119],"relatively":[122],"insensitive":[123],"rank-norm":[126],"parameter.":[127],"Via":[128],"proposed":[130,154],"method,":[134],"better":[135],"tradeoff":[136],"between":[137],"performance":[139,151],"robustness":[141],"can":[142,156],"be":[143,157],"provided.":[144],"Experimental":[145],"results":[146],"show":[147],"that":[148],"boosted":[158],"<inline-formula":[160],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[161],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">":[162],"<tex-math":[163],"notation=\"LaTeX\">$2\\%-11\\%$":[164],"</tex-math></inline-formula>":[165],",":[166],"compared":[167],"previous":[169],"methods.":[170]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
