{"id":"https://openalex.org/W2462198136","doi":"https://doi.org/10.1109/cbmi.2016.7500237","title":"Deep learning vs spectral clustering into an active clustering with pairwise constraints propagation","display_name":"Deep learning vs spectral clustering into an active clustering with pairwise constraints propagation","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2462198136","doi":"https://doi.org/10.1109/cbmi.2016.7500237","mag":"2462198136"},"language":"en","primary_location":{"id":"doi:10.1109/cbmi.2016.7500237","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbmi.2016.7500237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-01319969v1/file/Paper_CBMI2016_21.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061935721","display_name":"Nicolas Voiron","orcid":null},"institutions":[{"id":"https://openalex.org/I70900168","display_name":"Universit\u00e9 Savoie Mont Blanc","ror":"https://ror.org/04gqg1a07","country_code":"FR","type":"education","lineage":["https://openalex.org/I70900168"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Nicolas Voiron","raw_affiliation_strings":["LISTIC, Universit Savoie Mont Blanc, Annecy le Vieux, France"],"affiliations":[{"raw_affiliation_string":"LISTIC, Universit Savoie Mont Blanc, Annecy le Vieux, France","institution_ids":["https://openalex.org/I70900168"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067804280","display_name":"Alexandre Beno\u00eet","orcid":"https://orcid.org/0000-0002-0627-4948"},"institutions":[{"id":"https://openalex.org/I70900168","display_name":"Universit\u00e9 Savoie Mont Blanc","ror":"https://ror.org/04gqg1a07","country_code":"FR","type":"education","lineage":["https://openalex.org/I70900168"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Alexandre Benoit","raw_affiliation_strings":["LISTIC, Universit Savoie Mont Blanc, Annecy le Vieux, France"],"affiliations":[{"raw_affiliation_string":"LISTIC, Universit Savoie Mont Blanc, Annecy le Vieux, France","institution_ids":["https://openalex.org/I70900168"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091687210","display_name":"Patrick Lambert","orcid":"https://orcid.org/0000-0003-0478-9443"},"institutions":[{"id":"https://openalex.org/I70900168","display_name":"Universit\u00e9 Savoie Mont Blanc","ror":"https://ror.org/04gqg1a07","country_code":"FR","type":"education","lineage":["https://openalex.org/I70900168"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Patrick Lambert","raw_affiliation_strings":["LISTIC, Universit Savoie Mont Blanc, Annecy le Vieux, France"],"affiliations":[{"raw_affiliation_string":"LISTIC, Universit Savoie Mont Blanc, Annecy le Vieux, France","institution_ids":["https://openalex.org/I70900168"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051234459","display_name":"Bogdan Ionescu","orcid":"https://orcid.org/0000-0003-4112-5769"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Bogdan Ionescu","raw_affiliation_strings":["LAPI, University Politehnica of Bucharest, Bucharest, Romania"],"affiliations":[{"raw_affiliation_string":"LAPI, University Politehnica of Bucharest, Bucharest, Romania","institution_ids":["https://openalex.org/I61641377"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5061935721"],"corresponding_institution_ids":["https://openalex.org/I70900168"],"apc_list":null,"apc_paid":null,"fwci":0.8835,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.83933869,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"0","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9997000098228455,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9997000098228455,"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/T10057","display_name":"Face and Expression Recognition","score":0.9983999729156494,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9976000189781189,"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/cluster-analysis","display_name":"Cluster analysis","score":0.810246467590332},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7776724100112915},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7240030765533447},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6666936874389648},{"id":"https://openalex.org/keywords/conceptual-clustering","display_name":"Conceptual clustering","score":0.569499135017395},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.5517162084579468},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5378763675689697},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.5373150110244751},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.47622472047805786},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.47416558861732483},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.46360599994659424},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.45669183135032654},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.4079040586948395},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38025474548339844},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.29210364818573},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.15182310342788696}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.810246467590332},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7776724100112915},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7240030765533447},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6666936874389648},{"id":"https://openalex.org/C39235581","wikidata":"https://www.wikidata.org/wiki/Q5158434","display_name":"Conceptual clustering","level":5,"score":0.569499135017395},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.5517162084579468},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5378763675689697},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.5373150110244751},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.47622472047805786},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.47416558861732483},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.46360599994659424},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.45669183135032654},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.4079040586948395},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38025474548339844},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.29210364818573},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15182310342788696},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cbmi.2016.7500237","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbmi.2016.7500237","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-01319969v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01319969","pdf_url":"https://hal.science/hal-01319969v1/file/Paper_CBMI2016_21.pdf","source":{"id":"https://openalex.org/S4406922461","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cbmi2016.upb.ro/","raw_type":"Conference papers"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-01319969v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01319969","pdf_url":"https://hal.science/hal-01319969v1/file/Paper_CBMI2016_21.pdf","source":{"id":"https://openalex.org/S4406922461","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://cbmi2016.upb.ro/","raw_type":"Conference papers"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2462198136.pdf","grobid_xml":"https://content.openalex.org/works/W2462198136.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1570448133","https://openalex.org/W1595562640","https://openalex.org/W1616961769","https://openalex.org/W1806891645","https://openalex.org/W2010070281","https://openalex.org/W2011430131","https://openalex.org/W2087912899","https://openalex.org/W2088857627","https://openalex.org/W2115657355","https://openalex.org/W2132914434","https://openalex.org/W2135914502","https://openalex.org/W2141902614","https://openalex.org/W2153839362","https://openalex.org/W2157364932","https://openalex.org/W2163605009","https://openalex.org/W2164383578","https://openalex.org/W2912210943","https://openalex.org/W2963302148","https://openalex.org/W4235169531","https://openalex.org/W6626039241","https://openalex.org/W6633328910","https://openalex.org/W6634094483","https://openalex.org/W6635477647","https://openalex.org/W6683390034","https://openalex.org/W6683749178","https://openalex.org/W6684191040","https://openalex.org/W6758700058"],"related_works":["https://openalex.org/W2160785859","https://openalex.org/W4300978037","https://openalex.org/W2951567704","https://openalex.org/W2607137685","https://openalex.org/W4387506342","https://openalex.org/W3140018618","https://openalex.org/W2609148028","https://openalex.org/W2585341789","https://openalex.org/W1524712381","https://openalex.org/W2556924671"],"abstract_inverted_index":{"In":[0,94,127],"our":[1,125],"data":[2,48],"driven":[3],"world,":[4],"categorization":[5],"is":[6,69,90,119,168],"of":[7,46,51,58,112,135,179],"major":[8],"importance":[9],"to":[10,30,70,82,101,121],"help":[11],"end-users":[12],"and":[13,32,117,144,155],"decision":[14],"makers":[15],"understanding":[16],"information":[17],"structures.":[18],"Supervised":[19],"learning":[20,62,87],"techniques":[21,42],"rely":[22],"on":[23,170],"annotated":[24],"samples":[25],"that":[26],"are":[27],"often":[28,34,63],"difficult":[29],"obtain":[31],"training":[33,53],"overfits.":[35],"On":[36],"the":[37,44,47,56,59,113,133,177,180],"other":[38],"hand,":[39],"unsupervised":[40,65],"clustering":[41,142,181],"study":[43],"structure":[45],"without":[49],"disposing":[50],"any":[52],"data.":[54],"Given":[55],"difficulty":[57],"task,":[60],"supervised":[61],"outperforms":[64],"learning.":[66,93],"A":[67],"compromise":[68],"use":[71,96],"a":[72,77],"partial":[73],"knowledge,":[74],"selected":[75],"in":[76,80,124],"smart":[78],"way,":[79],"order":[81],"boost":[83],"performance":[84],"while":[85,162],"minimizing":[86],"costs,":[88],"what":[89],"called":[91],"semi-supervised":[92,141],"such":[95],"case,":[97],"Spectral":[98,148],"Clustering":[99],"proved":[100],"be":[102],"an":[103,139],"efficient":[104],"method.":[105],"Also,":[106],"Deep":[107,136],"Learning":[108,137],"outperformed":[109],"several":[110],"state":[111],"art":[114],"classification":[115],"approaches":[116],"it":[118,123,146,158],"interesting":[120],"test":[122],"context.":[126],"this":[128],"paper,":[129],"we":[130,151],"firstly":[131],"introduce":[132,152],"concept":[134],"into":[138],"active":[140],"process":[143],"compare":[145],"with":[147],"Clustering.":[149],"Secondly,":[150],"constraint":[153],"propagation":[154],"demonstrate":[156],"how":[157],"maximizes":[159],"partitioning":[160],"quality":[161],"reducing":[163],"annotation":[164],"costs.":[165],"Experimental":[166],"validation":[167],"conducted":[169],"two":[171],"different":[172],"real":[173],"datasets.":[174],"Results":[175],"show":[176],"potential":[178],"methods.":[182]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
