{"id":"https://openalex.org/W2807426458","doi":"https://doi.org/10.1142/s1469026818500116","title":"Similarity Learning Based on Sparse Representation for Semi-Supervised Boosting","display_name":"Similarity Learning Based on Sparse Representation for Semi-Supervised Boosting","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2807426458","doi":"https://doi.org/10.1142/s1469026818500116","mag":"2807426458"},"language":"en","primary_location":{"id":"doi:10.1142/s1469026818500116","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s1469026818500116","pdf_url":null,"source":{"id":"https://openalex.org/S206936884","display_name":"International Journal of Computational Intelligence and Applications","issn_l":"1469-0268","issn":["1469-0268","1757-5885"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311754","host_organization_name":"Imperial College Press","host_organization_lineage":["https://openalex.org/P4310311754"],"host_organization_lineage_names":["Imperial College Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence and Applications","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/A5115694806","display_name":"Qianying Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I113992204","display_name":"Hebei University of Economics and Business","ror":"https://ror.org/05j1kc284","country_code":"CN","type":"education","lineage":["https://openalex.org/I113992204"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qianying Wang","raw_affiliation_strings":["College of Mathematics and Statistics, Hebei University of Economics and Business, Shijiazhuang 050061, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mathematics and Statistics, Hebei University of Economics and Business, Shijiazhuang 050061, China","institution_ids":["https://openalex.org/I113992204"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102014973","display_name":"Ming Lu","orcid":"https://orcid.org/0000-0001-6174-7528"},"institutions":[{"id":"https://openalex.org/I94611258","display_name":"Hebei Normal University","ror":"https://ror.org/004rbbw49","country_code":"CN","type":"education","lineage":["https://openalex.org/I94611258"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ming Lu","raw_affiliation_strings":["College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050020, China","Hebei Key Laboratory of Computational Mathematics and Applications, Shijiazhuang 050020, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050020, China","institution_ids":["https://openalex.org/I94611258"]},{"raw_affiliation_string":"Hebei Key Laboratory of Computational Mathematics and Applications, Shijiazhuang 050020, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100782051","display_name":"Junhong Li","orcid":"https://orcid.org/0000-0002-4864-0897"},"institutions":[{"id":"https://openalex.org/I94611258","display_name":"Hebei Normal University","ror":"https://ror.org/004rbbw49","country_code":"CN","type":"education","lineage":["https://openalex.org/I94611258"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junhong Li","raw_affiliation_strings":["College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050020, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050020, China","institution_ids":["https://openalex.org/I94611258"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102014973"],"corresponding_institution_ids":["https://openalex.org/I94611258"],"apc_list":null,"apc_paid":null,"fwci":0.3181,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.61670087,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"17","issue":"02","first_page":"1850011","last_page":"1850011"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9987000226974487,"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":0.9987000226974487,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.9918000102043152,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9783999919891357,"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/boosting","display_name":"Boosting (machine learning)","score":0.7760884761810303},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7095947265625},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7007977962493896},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6538978219032288},{"id":"https://openalex.org/keywords/similarity-learning","display_name":"Similarity learning","score":0.5577763915061951},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.5182816386222839},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5106433033943176},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.4966784119606018},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.48185575008392334},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47269514203071594},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.45944368839263916},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4227641224861145},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.4194714426994324},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3134070634841919}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7760884761810303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7095947265625},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7007977962493896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6538978219032288},{"id":"https://openalex.org/C2779597229","wikidata":"https://www.wikidata.org/wiki/Q17146505","display_name":"Similarity learning","level":3,"score":0.5577763915061951},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.5182816386222839},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5106433033943176},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.4966784119606018},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.48185575008392334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47269514203071594},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.45944368839263916},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4227641224861145},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.4194714426994324},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3134070634841919},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s1469026818500116","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s1469026818500116","pdf_url":null,"source":{"id":"https://openalex.org/S206936884","display_name":"International Journal of Computational Intelligence and Applications","issn_l":"1469-0268","issn":["1469-0268","1757-5885"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310311754","host_organization_name":"Imperial College Press","host_organization_lineage":["https://openalex.org/P4310311754"],"host_organization_lineage_names":["Imperial College Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Intelligence and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321949","display_name":"Department of Education of Hebei Province","ror":"https://ror.org/01jkyjd96"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1981556116","https://openalex.org/W1986632995","https://openalex.org/W1988790447","https://openalex.org/W1997201895","https://openalex.org/W2001289057","https://openalex.org/W2070127246","https://openalex.org/W2145376937","https://openalex.org/W2161638512","https://openalex.org/W2164861862"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W4231274751","https://openalex.org/W2154063878","https://openalex.org/W132838958","https://openalex.org/W3205655893","https://openalex.org/W3081378822","https://openalex.org/W4313593186","https://openalex.org/W2013694281"],"abstract_inverted_index":{"Semi-supervised":[0],"boosting":[1,20,92,173],"strategy":[2],"aims":[3],"at":[4],"improving":[5],"the":[6,39,71,99,107,111,115,156,161,171,180],"performance":[7],"of":[8,15],"a":[9,13,22,32,49,128,145,150],"given":[10],"classifier":[11],"with":[12,60,175,182],"multitude":[14],"unlabeled":[16,28,40,55],"data.":[17],"In":[18],"semi-supervised":[19,80,91,137,172],"strategy,":[21],"similarity":[23,44,76,84,131,178],"is":[24,45,77,88,142,149],"needed":[25],"to":[26,38,47,54,69,121],"select":[27],"samples":[29,59,68],"and":[30],"then":[31],"pseudo":[33,52,62],"label":[34,53],"will":[35,64],"be":[36],"assigned":[37],"sample.":[41],"A":[42],"good":[43],"helpful":[46],"assign":[48],"more":[50],"proper":[51],"samples.":[56],"Those":[57],"selected":[58],"their":[61],"labels":[63],"serve":[65],"as":[66],"labeled":[67],"train":[70],"new":[72],"component":[73],"classifier.":[74],"So,":[75,124],"important":[78],"in":[79,90],"boosting.":[81,138],"Gaussian":[82,183],"kernel":[83,184],"[Formula:":[85,102,117],"see":[86,103,118],"text]":[87,104,119],"used":[89],"strategy.":[93],"There":[94],"are":[95],"two":[96],"drawbacks,":[97],"first,":[98],"Euclidean":[100],"distance":[101],"cannot":[105],"characterize":[106],"complicated":[108],"relationship":[109],"between":[110],"data":[112],"samples;":[113],"second,":[114],"parameter":[116],"needs":[120],"set":[122],"carefully.":[123],"this":[125],"paper":[126],"proposes":[127],"novel":[129],"adaptive":[130],"based":[132],"on":[133,164],"sparse":[134,140,176],"representation":[135,141,177],"for":[136],"Our":[139],"learned":[143],"from":[144,155],"\u201cclean\u201d":[146],"dictionary,":[147],"which":[148],"low":[151],"rank":[152],"matrix":[153],"obtained":[154],"sample":[157],"matrix.":[158],"We":[159],"evaluate":[160],"proposed":[162],"method":[163],"COIL20":[165],"databases.":[166],"Experimental":[167],"results":[168],"show":[169],"that:":[170],"algorithm":[174,181],"outperforms":[179],"similarity.":[185]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
