{"id":"https://openalex.org/W3112122414","doi":"https://doi.org/10.17635/lancaster/thesis/1206","title":"Subspace Clustering and Active Learning with Constraints","display_name":"Subspace Clustering and Active Learning with Constraints","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3112122414","doi":"https://doi.org/10.17635/lancaster/thesis/1206","mag":"3112122414"},"language":"en","primary_location":{"id":"pmh:oai:eprints.lancs.ac.uk:149795","is_oa":true,"landing_page_url":"https://eprints.lancs.ac.uk/id/eprint/149795/1/2020pengphd.pdf","pdf_url":"https://eprints.lancs.ac.uk/id/eprint/149795/1/2020pengphd.pdf","source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://eprints.lancs.ac.uk/id/eprint/149795/1/2020pengphd.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034725963","display_name":"Hankui Peng","orcid":"https://orcid.org/0000-0003-1623-9852"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Peng, Hankui","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-1623-9852","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5034725963"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9957000017166138,"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":0.9957000017166138,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9955000281333923,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9947999715805054,"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":0.8108633756637573},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6984519958496094},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5938657522201538},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.5824782848358154},{"id":"https://openalex.org/keywords/clustering-high-dimensional-data","display_name":"Clustering high-dimensional data","score":0.570276141166687},{"id":"https://openalex.org/keywords/data-stream-clustering","display_name":"Data stream clustering","score":0.5557125806808472},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.533008873462677},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.5256099700927734},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4877072274684906},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.47435224056243896},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4736267924308777},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.45090335607528687},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.4356954097747803},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.424938827753067},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4153374135494232},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.337502658367157}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8108633756637573},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6984519958496094},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5938657522201538},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.5824782848358154},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.570276141166687},{"id":"https://openalex.org/C193143536","wikidata":"https://www.wikidata.org/wiki/Q5227360","display_name":"Data stream clustering","level":5,"score":0.5557125806808472},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.533008873462677},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.5256099700927734},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4877072274684906},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.47435224056243896},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4736267924308777},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.45090335607528687},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.4356954097747803},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.424938827753067},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4153374135494232},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.337502658367157},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:eprints.lancs.ac.uk:149795","is_oa":true,"landing_page_url":"https://eprints.lancs.ac.uk/id/eprint/149795/1/2020pengphd.pdf","pdf_url":"https://eprints.lancs.ac.uk/id/eprint/149795/1/2020pengphd.pdf","source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},{"id":"doi:10.17635/lancaster/thesis/1206","is_oa":true,"landing_page_url":"https://doi.org/10.17635/lancaster/thesis/1206","pdf_url":null,"source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"},{"id":"mag:3112122414","is_oa":false,"landing_page_url":"https://eprints.lancs.ac.uk/id/eprint/149795/","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:eprints.lancs.ac.uk:149795","is_oa":true,"landing_page_url":"https://eprints.lancs.ac.uk/id/eprint/149795/1/2020pengphd.pdf","pdf_url":"https://eprints.lancs.ac.uk/id/eprint/149795/1/2020pengphd.pdf","source":{"id":"https://openalex.org/S4306401916","display_name":"Lancaster EPrints (Lancaster University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I67415387","host_organization_name":"Lancaster University","host_organization_lineage":["https://openalex.org/I67415387"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3112122414.pdf","grobid_xml":"https://content.openalex.org/works/W3112122414.grobid-xml"},"referenced_works_count":6,"referenced_works":["https://openalex.org/W2087962968","https://openalex.org/W2122825543","https://openalex.org/W2152322845","https://openalex.org/W2963840432","https://openalex.org/W2963846952","https://openalex.org/W3139328003"],"related_works":["https://openalex.org/W3008997528","https://openalex.org/W2804507487","https://openalex.org/W2966296698","https://openalex.org/W1978054115","https://openalex.org/W3106872790","https://openalex.org/W3000569786","https://openalex.org/W3090479701","https://openalex.org/W2963291430","https://openalex.org/W2972333410","https://openalex.org/W2093699483","https://openalex.org/W3011587229","https://openalex.org/W3034425668","https://openalex.org/W2088025572","https://openalex.org/W2766093974","https://openalex.org/W2963069872","https://openalex.org/W2918762554","https://openalex.org/W2619871843","https://openalex.org/W2610788802","https://openalex.org/W2909502737","https://openalex.org/W3097070873"],"abstract_inverted_index":{"Data":[0],"representations":[1],"can":[2,43,222,243,257],"often":[3,33],"be":[4,44,138,244,258],"high-dimensional,":[5],"whether":[6],"it":[7,242,319],"is":[8,32,87,100,164,267,282,302,310,359],"due":[9,20],"to":[10,21,76,110,123,137,140,198,232,347,361,373],"the":[11,23,34,37,41,60,69,73,88,121,135,227,268,285,298,316,327,330,337,353,363,389],"large":[12,322],"number":[13,323,379],"of":[14,40,62,84,90,129,161,237,264,270,287,312,324,380,391],"collected":[15],"/":[16],"recorded":[17],"features":[18],"or":[19,52],"how":[22],"data":[24,42,70,74,328],"sources":[25],"(e.g.":[26],"images,":[27],"texts)":[28],"are":[29],"processed.":[30],"It":[31,132,194,281],"case":[35],"that":[36,99,221,241],"main":[38],"structure":[39],"summarised":[45],"well":[46],"in":[47,68,112,153,246,326],"a":[48,91,126,165,183,217,233,321,342,378],"lower":[49,54],"dimensional":[50,55],"subspace":[51,66,79,143,175,185,205,273,344,382],"multiple":[53,65],"subspaces.":[56],"Subspace":[57,92,103],"clustering":[58,108,144,171,176,186,206,235,274,288,358,383,386,392],"addresses":[59],"problem":[61,228],"simultaneously":[63],"uncovering":[64],"structures":[67],"and":[71,115,151,169,211,305,384],"grouping":[72],"according":[75],"their":[77,293],"underlying":[78],"structures.":[80],"The":[81,158,261,307,367],"first":[82,317],"contribution":[83,160,263],"this":[85,162,179,265],"thesis":[86,163,266],"development":[89,269],"Clustering":[93,279],"with":[94,120,248],"Active":[95],"Learning":[96],"(SCAL)":[97],"framework":[98,106,172],"designed":[101],"for":[102,173,352],"Clustering.":[104],"This":[105],"allows":[107],"performance":[109,201,376],"improve":[111],"an":[113,271,349],"effective":[114],"efficient":[116],"manner":[117],"over":[118],"time,":[119],"need":[122],"query":[124],"only":[125],"small":[127],"amount":[128],"labelling":[130,255],"information.":[131],"also":[133,215],"has":[134,147,195,370],"potential":[136],"applied":[139,245,360],"more":[141],"general":[142],"methods,":[145],"which":[146,230,301],"been":[148,196,371],"further":[149],"explored":[150],"developed":[152],"our":[154,249],"next":[155],"methodological":[156],"contribution.":[157],"second":[159,338],"unified":[166],"active":[167],"learning":[168],"constrained":[170,234],"spectral-based":[174,184,204],"methods.":[177],"In":[178,315,336],"work,":[180],"we":[181,340],"propose":[182,216,341],"methodology":[187,275,309,369],"named":[188],"Weighted":[189],"Sparse":[190],"Simplex":[191],"Representation":[192],"(WSSR).":[193],"demonstrated":[197],"have":[199],"favourable":[200],"against":[202,377],"state-of-the-art":[203],"methods":[207],"on":[208,292,388],"both":[209,303],"synthetic":[210],"real":[212],"data.":[213],"We":[214,239],"flexible":[218],"weighting":[219],"scheme":[220],"incorporate":[223],"external":[224],"information":[225,256],"into":[226],"formulation,":[229],"leads":[231],"extension":[236],"WSSR.":[238],"show":[240],"conjunction":[247],"previously":[250],"proposed":[251,308,368],"SCAL":[252],"strategy":[253],"when":[254,295],"queried":[259],"sequentially.":[260],"third":[262],"algebraic":[272],"\u2013":[276],"Minimum":[277],"Angle":[278],"(MAC).":[280],"motivated":[283],"by":[284],"application":[286,390],"Amazon":[289,393],"products":[290],"based":[291],"titles":[294],"represented":[296],"using":[297],"TF-IDF":[299],"matrix,":[300],"sparse":[304],"high-dimensional.":[306],"composed":[311],"two":[313],"stages.":[314],"stage,":[318,339],"identifies":[320],"subspaces":[325,355],"through":[329],"Reduced":[331],"Row":[332],"Echelon":[333],"Form":[334],"technique.":[335],"new":[343],"proximity":[345],"measure":[346],"construct":[348],"affinity":[350],"matrix":[351],"formed":[354],"before":[356],"spectral":[357],"obtain":[362],"final":[364],"cluster":[365],"labels.":[366],"shown":[372],"enjoy":[374],"competitive":[375],"well-established":[381],"document":[385],"techniques":[387],"product":[394],"names.":[395]},"counts_by_year":[],"updated_date":"2026-08-14T07:06:38.338062","created_date":"2025-10-10T00:00:00"}
