{"id":"https://openalex.org/W2159345131","doi":"https://doi.org/10.1109/ijcnn.2004.1380143","title":"Self-enhanced relevant component analysis with side-information and unlabeled data","display_name":"Self-enhanced relevant component analysis with side-information and unlabeled data","publication_year":2005,"publication_date":"2005-04-05","ids":{"openalex":"https://openalex.org/W2159345131","doi":"https://doi.org/10.1109/ijcnn.2004.1380143","mag":"2159345131"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2004.1380143","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2004.1380143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)","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/A5090690232","display_name":"Fei Wu","orcid":"https://orcid.org/0000-0002-9366-2374"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fei Wu","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111464629","display_name":"Yonglei Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonglei Zhou","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5065063835","display_name":"Changshui Zhang","orcid":"https://orcid.org/0000-0002-8088-367X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changshui Zhang","raw_affiliation_strings":["Department of Automation, Tsinghua University, Beijing, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5090690232"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.3131,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.63244086,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"1347","last_page":"1351"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9905999898910522,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9829999804496765,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9661999940872192,"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/robustness","display_name":"Robustness (evolution)","score":0.7727817296981812},{"id":"https://openalex.org/keywords/component-analysis","display_name":"Component analysis","score":0.702929675579071},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.699129045009613},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5273041725158691},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5162153840065002},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4819338321685791},{"id":"https://openalex.org/keywords/data-space","display_name":"Data space","score":0.42252862453460693},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42050454020500183},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3782607913017273}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7727817296981812},{"id":"https://openalex.org/C2780692498","wikidata":"https://www.wikidata.org/wiki/Q16950721","display_name":"Component analysis","level":2,"score":0.702929675579071},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.699129045009613},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5273041725158691},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5162153840065002},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4819338321685791},{"id":"https://openalex.org/C2988382989","wikidata":"https://www.wikidata.org/wiki/Q370685","display_name":"Data space","level":2,"score":0.42252862453460693},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42050454020500183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3782607913017273},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"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},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2004.1380143","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2004.1380143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.188.934","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.188.934","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.washington.edu/homes/wufei/papers/IJCNN.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1512078904","https://openalex.org/W1620759536","https://openalex.org/W1871819883","https://openalex.org/W2065143024","https://openalex.org/W2069416420","https://openalex.org/W2114759290","https://openalex.org/W2117154949","https://openalex.org/W2125127226","https://openalex.org/W2134089414","https://openalex.org/W2153097561","https://openalex.org/W2153818524","https://openalex.org/W2159583439","https://openalex.org/W4237758037","https://openalex.org/W4285719527","https://openalex.org/W6630620170","https://openalex.org/W6636582384","https://openalex.org/W6677328822","https://openalex.org/W6679849079"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W4231274751","https://openalex.org/W2350806976","https://openalex.org/W2084935737","https://openalex.org/W2910914527","https://openalex.org/W2386547443","https://openalex.org/W2004583609","https://openalex.org/W2736297679","https://openalex.org/W2102113284"],"abstract_inverted_index":{"Relevant":[0],"component":[1,62],"analysis":[2,63],"(RCA)":[3],"is":[4],"a":[5,15,42,66],"powerful":[6],"tool":[7],"for":[8],"relevant":[9,61],"linear":[10],"feature":[11],"extraction":[12],"with":[13,98],"side-information,":[14],"new":[16],"focus":[17],"in":[18,45,51,69],"machine":[19],"learning":[20],"fields.":[21],"But":[22],"its":[23],"only":[24],"utilizing":[25],"positive":[26,39],"constraints":[27,40],"weakens":[28],"this":[29,49,52],"algorithm's":[30],"performance":[31],"and":[32,80],"robustness,":[33],"especially":[34],"when":[35],"there":[36],"are":[37],"few":[38],"-":[41],"common":[43],"case":[44],"practice.":[46],"To":[47],"overcome":[48],"drawback,":[50],"paper":[53],"we":[54],"propose":[55],"an":[56,94],"extended":[57],"algorithm":[58],"named":[59],"self-enhanced":[60],"(SERCA).":[64],"Through":[65],"boosting":[67],"procedure":[68],"the":[70,77],"product":[71],"space,":[72],"it":[73],"efficiently":[74],"uses":[75],"both":[76],"given":[78],"side-information":[79],"unlabeled":[81],"data.":[82],"The":[83],"experimental":[84],"results":[85],"on":[86],"several":[87],"data":[88],"sets":[89],"show":[90],"that":[91],"SERCA":[92],"achieves":[93],"obvious":[95],"improvement":[96],"compared":[97],"RCA.":[99]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
