{"id":"https://openalex.org/W3008997528","doi":"https://doi.org/10.1109/bigdata47090.2019.9006361","title":"Subspace Clustering with Active Learning","display_name":"Subspace Clustering with Active Learning","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008997528","doi":"https://doi.org/10.1109/bigdata47090.2019.9006361","mag":"3008997528"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006361","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5034725963","display_name":"Hankui Peng","orcid":"https://orcid.org/0000-0003-1623-9852"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Hankui Peng","raw_affiliation_strings":["STOR-i Centre for Doctoral Training, Lancaster University, Lancaster, UK"],"affiliations":[{"raw_affiliation_string":"STOR-i Centre for Doctoral Training, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033673770","display_name":"Nicos G. Pavlidis","orcid":"https://orcid.org/0000-0002-0301-5350"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nicos G. Pavlidis","raw_affiliation_strings":["Department of Management Science, Lancaster University, Lancaster, UK"],"affiliations":[{"raw_affiliation_string":"Department of Management Science, Lancaster University, Lancaster, UK","institution_ids":["https://openalex.org/I67415387"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5034725963"],"corresponding_institution_ids":["https://openalex.org/I67415387"],"apc_list":null,"apc_paid":null,"fwci":0.4087,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.67479508,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"135","last_page":"144"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9976999759674072,"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":0.9976999759674072,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9957000017166138,"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":0.9933000206947327,"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/cluster-analysis","display_name":"Cluster analysis","score":0.6964982748031616},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6852930784225464},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4718746244907379},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45452654361724854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34753626585006714}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6964982748031616},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6852930784225464},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4718746244907379},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45452654361724854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34753626585006714}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006361","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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":36,"referenced_works":["https://openalex.org/W79405465","https://openalex.org/W1528361845","https://openalex.org/W1533128638","https://openalex.org/W1549658602","https://openalex.org/W1693085440","https://openalex.org/W1978633512","https://openalex.org/W1993962865","https://openalex.org/W2022605463","https://openalex.org/W2048603976","https://openalex.org/W2052311585","https://openalex.org/W2080021732","https://openalex.org/W2091507279","https://openalex.org/W2098203240","https://openalex.org/W2099111195","https://openalex.org/W2125874614","https://openalex.org/W2128518360","https://openalex.org/W2128678390","https://openalex.org/W2134342006","https://openalex.org/W2165916500","https://openalex.org/W2170728413","https://openalex.org/W2210387432","https://openalex.org/W2222512263","https://openalex.org/W2243952159","https://openalex.org/W2605991684","https://openalex.org/W2783468591","https://openalex.org/W2964051179","https://openalex.org/W3021963533","https://openalex.org/W3023058184","https://openalex.org/W4298082496","https://openalex.org/W4300514451","https://openalex.org/W6603183647","https://openalex.org/W6632797712","https://openalex.org/W6678459319","https://openalex.org/W6679227803","https://openalex.org/W6721804939","https://openalex.org/W6736502011"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4224009465","https://openalex.org/W1999627569","https://openalex.org/W2380998760","https://openalex.org/W763609066","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W3046775127","https://openalex.org/W4205958290"],"abstract_inverted_index":{"Subspace":[0],"clustering":[1,84,99,138,167],"is":[2,54,140,163],"a":[3,38,59,69],"growing":[4],"field":[5],"of":[6,40,62,113,124,192],"unsupervised":[7],"learning":[8,95],"that":[9,34,58,73,100,142,159],"has":[10],"gained":[11],"much":[12],"popularity":[13],"in":[14,23],"the":[15,44,48,83,107,114,121,145,150,154,190],"computer":[16],"vision":[17],"community.":[18],"Applications":[19],"can":[20,64,74],"be":[21,65],"found":[22],"areas":[24],"such":[25],"as":[26],"motion":[27,180],"segmentation":[28,181],"and":[29,42,76,105,131,172,184],"face":[30],"clustering.":[31],"It":[32],"assumes":[33],"data":[35,45,178,182,187],"originate":[36],"from":[37,120],"union":[39],"subspaces,":[41],"clusters":[43],"depending":[46],"on":[47,118,176],"corresponding":[49],"subspace.":[50],"In":[51,88],"practice,":[52],"it":[53],"reasonable":[55],"to":[56,81,128,149],"assume":[57],"limited":[60],"amount":[61],"labels":[63],"obtained,":[66],"potentially":[67,132],"at":[68],"cost.":[70],"Therefore,":[71],"algorithms":[72,168],"effectively":[75],"efficiently":[77],"incorporate":[78],"this":[79,89],"information":[80],"improve":[82],"model":[85],"are":[86],"desirable.":[87],"paper,":[90],"we":[91],"propose":[92],"an":[93],"active":[94,195],"framework":[96,116,162],"for":[97,165],"subspace":[98,108,137,166],"sequentially":[101],"queries":[102],"informative":[103],"points":[104],"updates":[106],"model.":[109],"The":[110],"query":[111],"stage":[112],"proposed":[115,141,161,194],"relies":[117],"results":[119],"perturbation":[122],"theory":[123],"principal":[125],"component":[126],"analysis,":[127],"identify":[129],"influential":[130],"misclassified":[133],"points.":[134],"A":[135],"constrained":[136],"algorithm":[139],"monotonically":[143],"decreases":[144],"objective":[146],"function":[147],"subject":[148],"constraints":[151],"imposed":[152],"by":[153],"labelled":[155],"data.":[156],"We":[157],"show":[158],"our":[160,193],"suitable":[164],"including":[169],"iterative":[170],"methods":[171],"spectral":[173],"methods.":[174],"Experiments":[175],"synthetic":[177],"sets,":[179,183],"Yale":[185],"Faces":[186],"sets":[188],"demonstrate":[189],"advantage":[191],"strategy":[196],"over":[197],"state-of-the-art.":[198]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
