{"id":"https://openalex.org/W3098549646","doi":"https://doi.org/10.1109/tetci.2020.3035409","title":"On Robust Grouping Active Learning","display_name":"On Robust Grouping Active Learning","publication_year":2020,"publication_date":"2020-11-11","ids":{"openalex":"https://openalex.org/W3098549646","doi":"https://doi.org/10.1109/tetci.2020.3035409","mag":"3098549646"},"language":"en","primary_location":{"id":"doi:10.1109/tetci.2020.3035409","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2020.3035409","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-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/A5100330875","display_name":"Changsheng Li","orcid":"https://orcid.org/0000-0001-9789-7632"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Changsheng Li","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100350377","display_name":"Yang Chen","orcid":"https://orcid.org/0000-0001-8697-003X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Yang","raw_affiliation_strings":["School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112801316","display_name":"Lingyan Liang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210144143","display_name":"Inspur (China)","ror":"https://ror.org/0474p4r72","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210144143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingyan Liang","raw_affiliation_strings":["State Key Laboratory of High-End Server & Storage Technology, Inspur Group Company Limited, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of High-End Server & Storage Technology, Inspur Group Company Limited, Beijing, China","institution_ids":["https://openalex.org/I4210144143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014346487","display_name":"Ye Yuan","orcid":"https://orcid.org/0000-0002-0247-9866"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Yuan","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054991337","display_name":"Guoren Wang","orcid":"https://orcid.org/0000-0002-0181-8379"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoren Wang","raw_affiliation_strings":["School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100330875"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.9279,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.80841578,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"6","issue":"1","first_page":"103","last_page":"112"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9998999834060669,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9993000030517578,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9943000078201294,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6986250877380371},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6404107809066772},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5893182754516602},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.5381006002426147},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.534843385219574},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5117621421813965},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.45862311124801636},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.43411383032798767},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.420502245426178},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4167686104774475},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3963898718357086},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.35209518671035767},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.17288076877593994},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.09767848253250122}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6986250877380371},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6404107809066772},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5893182754516602},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.5381006002426147},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.534843385219574},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5117621421813965},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.45862311124801636},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.43411383032798767},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.420502245426178},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4167686104774475},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3963898718357086},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.35209518671035767},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.17288076877593994},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.09767848253250122},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tetci.2020.3035409","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tetci.2020.3035409","pdf_url":null,"source":{"id":"https://openalex.org/S4210210251","display_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","issn_l":"2471-285X","issn":["2471-285X"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Emerging Topics in Computational Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4223126646","display_name":null,"funder_award_id":"61932004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G511195426","display_name":null,"funder_award_id":"61732003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G681710594","display_name":null,"funder_award_id":"61806044","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":60,"referenced_works":["https://openalex.org/W37083855","https://openalex.org/W185973768","https://openalex.org/W1528361845","https://openalex.org/W1773652845","https://openalex.org/W1942758450","https://openalex.org/W1982039810","https://openalex.org/W1997201895","https://openalex.org/W2001141328","https://openalex.org/W2006533296","https://openalex.org/W2009134948","https://openalex.org/W2012878613","https://openalex.org/W2018571751","https://openalex.org/W2021469588","https://openalex.org/W2025566624","https://openalex.org/W2034014085","https://openalex.org/W2035128422","https://openalex.org/W2053186076","https://openalex.org/W2054271410","https://openalex.org/W2088025572","https://openalex.org/W2090641502","https://openalex.org/W2100916003","https://openalex.org/W2109751703","https://openalex.org/W2113741880","https://openalex.org/W2114188922","https://openalex.org/W2117907414","https://openalex.org/W2122992840","https://openalex.org/W2123921160","https://openalex.org/W2129812935","https://openalex.org/W2132852291","https://openalex.org/W2135012301","https://openalex.org/W2142890639","https://openalex.org/W2154872931","https://openalex.org/W2158515176","https://openalex.org/W2167828456","https://openalex.org/W2184866251","https://openalex.org/W2194775991","https://openalex.org/W2533488116","https://openalex.org/W2592232824","https://openalex.org/W2608346273","https://openalex.org/W2735531870","https://openalex.org/W2741485222","https://openalex.org/W2777262900","https://openalex.org/W2794825826","https://openalex.org/W2810921114","https://openalex.org/W2897563637","https://openalex.org/W2905927259","https://openalex.org/W2951911250","https://openalex.org/W2962745799","https://openalex.org/W2962750252","https://openalex.org/W2964002344","https://openalex.org/W3102785203","https://openalex.org/W4292363360","https://openalex.org/W4376601268","https://openalex.org/W6601518771","https://openalex.org/W6607574751","https://openalex.org/W6640786210","https://openalex.org/W6677657710","https://openalex.org/W6686862989","https://openalex.org/W6741783443","https://openalex.org/W6929385289"],"related_works":["https://openalex.org/W2172836935","https://openalex.org/W2066259560","https://openalex.org/W2355203151","https://openalex.org/W2772780115","https://openalex.org/W2765107592","https://openalex.org/W1967237222","https://openalex.org/W2551841573","https://openalex.org/W2158762543","https://openalex.org/W3156483363","https://openalex.org/W3011145085"],"abstract_inverted_index":{"Early":[0],"active":[1,93],"learning,":[2],"in":[3,25,126,137],"a":[4,138,157,171,178,185],"common":[5],"paradigm,":[6],"usually":[7],"selects":[8],"representative":[9,78],"samples":[10,36,54,79],"for":[11,191],"human":[12,205],"annotating.":[13],"This":[14],"aligns":[15],"with":[16,153,188],"the":[17,21,50,72,81,145,224],"goal":[18],"of":[19,116,119,148,173,226],"minimizing":[20],"overall":[22],"reconstruction":[23,174],"error":[24],"an":[26,91],"unsupervised":[27,92],"manner.":[28],"While":[29],"existing":[30],"methods":[31],"mainly":[32],"focus":[33],"on":[34,170,198,219],"data":[35,123,130,146],"that":[37,144],"are":[38,55],"drawn":[39,61],"from":[40,62,131],"individual":[41],"yet":[42],"high-dimensional":[43],"feature":[44],"space,":[45,140,181],"they":[46],"can":[47,134,151],"hardly":[48],"handle":[49],"real-world":[51],"scenario":[52],"where":[53],"often":[56],"represented":[57],"by":[58,166],"low-dimensional":[59,139],"features":[60],"multiple":[63,199],"groups":[64,150],"(subspaces).":[65],"In":[66,86],"this":[67,87,106],"case,":[68],"how":[69],"to":[70,75,84,104,112,156],"leverage":[71],"grouping":[73],"structure":[74],"select":[76],"most":[77],"becomes":[80],"key":[82,109],"point":[83],"success.":[85],"paper,":[88],"we":[89,128,182],"propose":[90],"learning":[94],"framework,":[95],"called":[96],"<italic":[97],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[98],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">Robust":[99],"Grouping":[100],"Active":[101],"Learning</i>":[102],"(RGAL),":[103],"achieve":[105],"goal.":[107],"The":[108],"idea":[110],"is":[111],"take":[113],"into":[114],"account":[115],"different":[117,149],"degrees":[118],"information":[120],"shared":[121],"across":[122],"groups.":[124],"Specifically":[125],"RGAL,":[127],"assume":[129],"some":[132],"group":[133,164],"be":[135],"embedded":[136],"as":[141,143],"well":[142],"distributions":[147],"overlap":[152],"each":[154],"other":[155],"certain":[158],"degree.":[159],"And":[160],"RGAL":[161,228],"controls":[162],"such":[163],"overlaps":[165],"imposing":[167],"sparsity":[168],"constraints":[169],"matrix":[172],"coefficients.":[175],"To":[176],"encourage":[177],"smooth":[179],"coefficient":[180],"also":[183],"enforce":[184],"robust":[186],"loss":[187],"Laplacian":[189],"regularization":[190],"noise":[192],"suppression.":[193],"We":[194],"perform":[195],"extensive":[196],"experiments":[197],"tasks":[200],"which":[201],"normally":[202],"require":[203],"costly":[204],"annotation,":[206],"including":[207],"facial":[208],"age":[209],"estimation,":[210],"video":[211],"action":[212],"recognition":[213],"and":[214],"medical":[215],"image":[216],"classification.":[217],"Results":[218],"benchmark":[220],"datasets":[221],"clearly":[222],"demonstrate":[223],"efficacy":[225],"our":[227],"method":[229],"compared":[230],"state-of-the-art":[231],"methods.":[232]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
