{"id":"https://openalex.org/W2908322374","doi":"https://doi.org/10.1109/tip.2018.2889960","title":"Correntropy-Induced Robust Low-Rank Hypergraph","display_name":"Correntropy-Induced Robust Low-Rank Hypergraph","publication_year":2018,"publication_date":"2018-12-27","ids":{"openalex":"https://openalex.org/W2908322374","doi":"https://doi.org/10.1109/tip.2018.2889960","mag":"2908322374","pmid":"https://pubmed.ncbi.nlm.nih.gov/30596578"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2018.2889960","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2889960","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5036950222","display_name":"Taisong Jin","orcid":"https://orcid.org/0000-0002-8873-1814"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Taisong Jin","raw_affiliation_strings":["Department of Computer Science, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0002-8873-1814","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016080094","display_name":"Rongrong Ji","orcid":"https://orcid.org/0000-0001-9163-2932"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rongrong Ji","raw_affiliation_strings":["Department of Cognitive Science, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0001-9163-2932","affiliations":[{"raw_affiliation_string":"Department of Cognitive Science, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100602494","display_name":"Yue Gao","orcid":"https://orcid.org/0000-0002-4971-590X"},"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":"Yue Gao","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4971-590X","affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059926864","display_name":"Xiaoshuai Sun","orcid":"https://orcid.org/0000-0003-3912-9306"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoshuai Sun","raw_affiliation_strings":["Department of Cyber Space Security, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Cyber Space Security, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100773043","display_name":"Xibin Zhao","orcid":"https://orcid.org/0000-0002-6168-7016"},"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":"Xibin Zhao","raw_affiliation_strings":["School of Software, Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074103823","display_name":"Dacheng Tao","orcid":"https://orcid.org/0000-0001-7225-5449"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Dacheng Tao","raw_affiliation_strings":["Faculty of Engineering and Information Technologies, The University of Sydney, Darlington, NSW, Australia"],"raw_orcid":"https://orcid.org/0000-0001-7225-5449","affiliations":[{"raw_affiliation_string":"Faculty of Engineering and Information Technologies, The University of Sydney, Darlington, NSW, Australia","institution_ids":["https://openalex.org/I129604602"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8046,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.90814196,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":99},"biblio":{"volume":"28","issue":"6","first_page":"2755","last_page":"2769"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.998199999332428,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9936000108718872,"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/hypergraph","display_name":"Hypergraph","score":0.9221414923667908},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6432977914810181},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48569783568382263},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4522939920425415},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44305887818336487},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.42805713415145874},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4232773780822754},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.42062073945999146},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.41009584069252014},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3749091625213623}],"concepts":[{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.9221414923667908},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6432977914810181},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48569783568382263},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4522939920425415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44305887818336487},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42805713415145874},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4232773780822754},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.42062073945999146},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.41009584069252014},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3749091625213623},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2018.2889960","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2018.2889960","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Image Processing","raw_type":"journal-article"},{"id":"pmid:30596578","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/30596578","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:opus.lib.uts.edu.au:10453/138354","is_oa":false,"landing_page_url":"http://hdl.handle.net/10453/138354","pdf_url":null,"source":{"id":"https://openalex.org/S4306401357","display_name":"UTS ePRESS (University of Technology Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114017466","host_organization_name":"University of Technology Sydney","host_organization_lineage":["https://openalex.org/I114017466"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Journal Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1353530241","display_name":null,"funder_award_id":"U1705262","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2594239301","display_name":"\u6076\u52a3\u73af\u5883\u4e0b\u9065\u611f\u56fe\u50cf\u5c0f\u76ee\u6807\u9c81\u68d2\u68c0\u6d4b\u7b97\u6cd5\u7814\u7a76","funder_award_id":"61772443","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G470717166","display_name":null,"funder_award_id":"2018J01106","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"},{"id":"https://openalex.org/G5495072040","display_name":null,"funder_award_id":"2017J01125","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"},{"id":"https://openalex.org/G5564659648","display_name":null,"funder_award_id":"2017M612134","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G7529411012","display_name":"\u57fa\u4e8e\u9690\u5f0f\u5206\u89e3\u7684\u65f6\u95f4\u5e8f\u5217\u7ed3\u6784\u6a21\u578b\u7684\u63a8\u7406\u4e0e\u9884\u6d4b\u7814\u7a76","funder_award_id":"61572410","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8440137829","display_name":"\u9762\u5411\u8d44\u6e90\u7a00\u7f3a\u578b\u8bed\u8a00\u7684\u673a\u5668\u7ffb\u8bd1\u7406\u8bba\u65b9\u6cd5\u53ca\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61732005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8507526577","display_name":null,"funder_award_id":"61702136","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1529019671","https://openalex.org/W1600550542","https://openalex.org/W1736339626","https://openalex.org/W1954735160","https://openalex.org/W1989889610","https://openalex.org/W1993962865","https://openalex.org/W1994761691","https://openalex.org/W1997201895","https://openalex.org/W1998840160","https://openalex.org/W2006904655","https://openalex.org/W2009226074","https://openalex.org/W2020400084","https://openalex.org/W2051090063","https://openalex.org/W2052575990","https://openalex.org/W2057522815","https://openalex.org/W2067922550","https://openalex.org/W2068078373","https://openalex.org/W2069421937","https://openalex.org/W2081687758","https://openalex.org/W2100549954","https://openalex.org/W2100659887","https://openalex.org/W2104819583","https://openalex.org/W2118120419","https://openalex.org/W2118137856","https://openalex.org/W2121947440","https://openalex.org/W2125874614","https://openalex.org/W2135160607","https://openalex.org/W2137823674","https://openalex.org/W2138910149","https://openalex.org/W2143685245","https://openalex.org/W2145152441","https://openalex.org/W2147021384","https://openalex.org/W2158579916","https://openalex.org/W2165423399","https://openalex.org/W2203392907","https://openalex.org/W2288381231","https://openalex.org/W2322271577","https://openalex.org/W2343516804","https://openalex.org/W2462037541","https://openalex.org/W2462350434","https://openalex.org/W2468752595","https://openalex.org/W2576821058","https://openalex.org/W2761013467","https://openalex.org/W2802754347","https://openalex.org/W2951085447","https://openalex.org/W2962911132","https://openalex.org/W2963630677","https://openalex.org/W3024853581","https://openalex.org/W3122665668","https://openalex.org/W6635552349","https://openalex.org/W6675164516","https://openalex.org/W6677677282","https://openalex.org/W6681767761","https://openalex.org/W6929385289"],"related_works":["https://openalex.org/W4376608589","https://openalex.org/W1537073411","https://openalex.org/W3138003926","https://openalex.org/W1630514295","https://openalex.org/W4300037846","https://openalex.org/W4288275998","https://openalex.org/W2963081352","https://openalex.org/W1970319972","https://openalex.org/W2953254336","https://openalex.org/W2112030392"],"abstract_inverted_index":{"Hypergraph":[0],"learning":[1,43,64],"has":[2],"been":[3],"widely":[4],"exploited":[5],"in":[6,15,173],"various":[7,71],"image":[8,177,181],"processing":[9],"applications,":[10],"due":[11],"to":[12,30,47,82,110,119,129,139,155],"its":[13],"advantages":[14],"modeling":[16],"the":[17,35,40,53,88,97,112,123,132,141,145,150,163,169,174,188,191],"high-order":[18],"information.":[19],"Its":[20],"efficacy":[21],"highly":[22],"depends":[23],"on":[24,195],"building":[25],"an":[26],"informative":[27],"hypergraph":[28,42,63,171,193],"structure":[29,92],"accurately":[31],"and":[32,179],"robustly":[33],"formulate":[34],"underlying":[36],"data":[37,91],"correlation.":[38],"However,":[39],"existing":[41],"methods":[44],"are":[45,153],"sensitive":[46],"non-":[48,72],"Gaussian":[49,73],"noise,":[50],"which":[51,66,86,115,135],"hurts":[52],"corresponding":[54],"performance.":[55],"In":[56,75],"this":[57],"paper,":[58],"we":[59],"present":[60],"a":[61,84,106,157],"noise-resistant":[62],"model,":[65],"provides":[67],"superior":[68],"robustness":[69],"against":[70],"noises.":[74,121],"particular,":[76],"our":[77,137,184],"model":[78,138,172],"adopts":[79],"low-rank":[80,133],"representation":[81],"construct":[83],"hypergraph,":[85],"captures":[87],"globally":[89],"linear":[90],"as":[93,95,160,162],"well":[94,161],"preserving":[96],"grouping":[98],"effect":[99],"of":[100,144,176,190],"highly-correlated":[101],"data.":[102],"We":[103,166],"further":[104],"introduce":[105],"correntropyinduced":[107],"local":[108],"metric":[109],"measure":[111],"reconstruction":[113],"errors,":[114],"is":[116,127],"particularly":[117],"robust":[118],"non-Gaussian":[120],"Finally,":[122],"Frobenious-norm":[124],"based":[125],"regularization":[126],"proposed":[128,170],"combine":[130],"with":[131],"regularizer,":[134],"enables":[136],"regularize":[140],"singular":[142],"values":[143],"coefficient":[146],"matrix.":[147],"By":[148],"such,":[149],"non-zero":[151],"coefficients":[152],"selected":[154],"generate":[156],"hyperedge":[158,164],"set":[159],"weights.":[165],"have":[167],"evaluated":[168],"tasks":[175],"clustering":[178],"semi-supervised":[180],"classification.":[182],"Quantitatively,":[183],"scheme":[185],"significantly":[186],"enhances":[187],"performance":[189],"state-of-the-art":[192],"models":[194],"several":[196],"benchmark":[197],"datasets.":[198]},"counts_by_year":[{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}