{"id":"https://openalex.org/W2963286139","doi":"https://doi.org/10.1109/cvpr.2019.00627","title":"A Decomposition Algorithm for the Sparse Generalized Eigenvalue Problem","display_name":"A Decomposition Algorithm for the Sparse Generalized Eigenvalue Problem","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2963286139","doi":"https://doi.org/10.1109/cvpr.2019.00627","mag":"2963286139"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2019.00627","doi":"https://doi.org/10.1109/cvpr.2019.00627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2019.00627","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1802.09303","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5040011580","display_name":"Ganzhao Yuan","orcid":"https://orcid.org/0000-0002-2239-7315","observed_orcids":["https://orcid.org/0000-0002-2239-7315"]},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]},{"id":"https://openalex.org/I4210136793","display_name":"Peng Cheng Laboratory","ror":"https://ror.org/03qdqbt06","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210136793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ganzhao Yuan","raw_affiliation_strings":["Sun Yat-Sen Univ","Center for Quantum Computing, Peng Cheng Laboratory, Shenzhen, China","Ministry of Education, Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), China","School of Data and Computer Science, Sun Yat-sen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen Univ","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Center for Quantum Computing, Peng Cheng Laboratory, Shenzhen, China","institution_ids":["https://openalex.org/I4210136793"]},{"raw_affiliation_string":"Ministry of Education, Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042802004","display_name":"Li Shen","orcid":"https://orcid.org/0000-0001-5659-3464","observed_orcids":["https://orcid.org/0000-0001-5659-3464"]},"institutions":[{"id":"https://openalex.org/I2250653659","display_name":"Tencent (China)","ror":"https://ror.org/00hhjss72","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250653659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Shen","raw_affiliation_strings":["Tencent AI Lab","Tencent AI Lab, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tencent AI Lab","institution_ids":["https://openalex.org/I2250653659"]},{"raw_affiliation_string":"Tencent AI Lab, Shenzhen, China","institution_ids":["https://openalex.org/I2250653659"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108050904","display_name":"Wei\u2010Shi Zheng","orcid":"https://orcid.org/0000-0001-8327-0003","observed_orcids":["https://orcid.org/0000-0001-8327-0003"]},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei-Shi Zheng","raw_affiliation_strings":["Sun Yat-sen Univ","Ministry of Education, Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), China","School of Data and Computer Science, Sun Yat-sen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-sen Univ","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Ministry of Education, Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.9114,"has_fulltext":true,"cited_by_count":10,"citation_normalized_percentile":{"value":0.93002137,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"6106","last_page":"6115"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.8179000020027161,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.8179000020027161,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.10040000081062317,"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/T10792","display_name":"Matrix Theory and Algorithms","score":0.029100000858306885,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/decomposition-method","display_name":"decomposition method","score":0.9129999876022339},{"id":"https://openalex.org/keywords/sparse-principal-component-analysis","display_name":"sparse principal component analysis","score":0.8799999952316284},{"id":"https://openalex.org/keywords/sparse-canonical-correlation-analysis","display_name":"sparse canonical correlation analysis","score":1.0},{"id":"https://openalex.org/keywords/quadratic-fractional-programming","display_name":"quadratic fractional programming","score":0.8240000009536743},{"id":"https://openalex.org/keywords/bisection-search","display_name":"bisection search","score":0.8140000104904175},{"id":"https://openalex.org/keywords/coordinate-descent","display_name":"coordinate descent","score":0.996999979019165},{"id":"https://openalex.org/keywords/combinatorial-search","display_name":"combinatorial search","score":0.7979999780654907},{"id":"https://openalex.org/keywords/np-hard-optimization-problems","display_name":"NP-hard optimization","score":0.6000000238418579}],"concepts":[{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.589390754699707},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5825381278991699},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.57841956615448},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5398532748222351},{"id":"https://openalex.org/C169756996","wikidata":"https://www.wikidata.org/wiki/Q194919","display_name":"Eigendecomposition of a matrix","level":3,"score":0.532617449760437},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4923652410507202},{"id":"https://openalex.org/C81845259","wikidata":"https://www.wikidata.org/wiki/Q290117","display_name":"Quadratic programming","level":2,"score":0.47935038805007935},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4729616045951843},{"id":"https://openalex.org/C24252448","wikidata":"https://www.wikidata.org/wiki/Q7573786","display_name":"Sparse PCA","level":3,"score":0.4422149360179901},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.43948161602020264},{"id":"https://openalex.org/C157553263","wikidata":"https://www.wikidata.org/wiki/Q5168004","display_name":"Coordinate descent","level":2,"score":0.43591347336769104},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.4334816336631775},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4327704906463623},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.40988242626190186},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25310850143432617},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cvpr.2019.00627","doi":"https://doi.org/10.1109/cvpr.2019.00627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2019.00627","pdf_url":null,"source":{"id":"https://openalex.org/S4210176548","display_name":"IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings","issn_l":"1063-6919","issn":["1063-6919","2332-564X","2575-7075"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1802.09303","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1802.09303","pdf_url":"https://arxiv.org/pdf/1802.09303","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"doi:10.48550/arxiv.1802.09303","doi":"https://doi.org/10.48550/arxiv.1802.09303","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1802.09303","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1802.09303","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1802.09303","pdf_url":"https://arxiv.org/pdf/1802.09303","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2963286139.pdf","grobid_xml":"https://content.openalex.org/works/W2963286139.grobid-xml"},"referenced_works_count":50,"referenced_works":["https://openalex.org/W37129894","https://openalex.org/W184430818","https://openalex.org/W391578156","https://openalex.org/W1540764732","https://openalex.org/W1576520375","https://openalex.org/W1577337550","https://openalex.org/W1599867596","https://openalex.org/W1615739705","https://openalex.org/W1733665134","https://openalex.org/W1924300989","https://openalex.org/W1967956032","https://openalex.org/W1975900269","https://openalex.org/W1977899734","https://openalex.org/W1979089199","https://openalex.org/W1979817396","https://openalex.org/W2009455267","https://openalex.org/W2022712430","https://openalex.org/W2083346837","https://openalex.org/W2090579338","https://openalex.org/W2097417531","https://openalex.org/W2098854771","https://openalex.org/W2110096996","https://openalex.org/W2113600901","https://openalex.org/W2116742884","https://openalex.org/W2137404238","https://openalex.org/W2140214428","https://openalex.org/W2144629557","https://openalex.org/W2148036851","https://openalex.org/W2148980251","https://openalex.org/W2153635508","https://openalex.org/W2158281641","https://openalex.org/W2164251185","https://openalex.org/W2165966284","https://openalex.org/W2171270653","https://openalex.org/W2229285313","https://openalex.org/W2296319761","https://openalex.org/W2328936146","https://openalex.org/W2337119602","https://openalex.org/W2337541833","https://openalex.org/W2413501549","https://openalex.org/W2556206132","https://openalex.org/W2615253071","https://openalex.org/W2703574989","https://openalex.org/W2950988875","https://openalex.org/W2962763638","https://openalex.org/W2963763664","https://openalex.org/W2964196814","https://openalex.org/W3014542904","https://openalex.org/W3041093287","https://openalex.org/W4250736117"],"related_works":["https://openalex.org/W4386148124","https://openalex.org/W1979817396","https://openalex.org/W1675224738","https://openalex.org/W1995249482","https://openalex.org/W3013825221","https://openalex.org/W1977899734","https://openalex.org/W2768018294","https://openalex.org/W2963763664","https://openalex.org/W2306323002","https://openalex.org/W2166604943"],"abstract_inverted_index":{"The":[0],"sparse":[1,17,21,26],"generalized":[2],"eigenvalue":[3],"problem":[4,32],"arises":[5],"in":[6,126],"a":[7,46,64,80,85],"number":[8],"of":[9,76,128],"standard":[10],"and":[11,25,67,84,112,121],"modern":[12],"statistical":[13],"learning":[14],"models,":[15],"including":[16],"principal":[18],"component":[19],"analysis,":[20,24],"Fisher":[22],"discriminant":[23],"canonical":[27],"correlation":[28],"analysis.":[29],"However,":[30],"this":[31,42,53],"is":[33,39],"difficult":[34],"to":[35,51,62],"solve":[36],"since":[37],"it":[38],"NP-hard.":[40],"In":[41,96],"paper,":[43],"we":[44,56,98],"consider":[45,79],"new":[47],"effective":[48],"decomposition":[49],"method":[50,83,88,119],"tackle":[52],"problem.":[54],"Specifically,":[55],"use":[57],"random":[58],"or/and":[59],"swapping":[60],"strategies":[61],"find":[63],"working":[65],"set":[66],"perform":[68],"global":[69],"combinatorial":[70],"search":[71,82],"over":[72],"the":[73,91,104],"small":[74],"subset":[75],"variables.":[77],"We":[78],"bisection":[81],"coordinate":[86],"descent":[87],"for":[89,103],"solving":[90],"quadratic":[92],"fractional":[93],"programming":[94],"subproblem.":[95],"addition,":[97],"provide":[99],"some":[100],"theoretical":[101],"analysis":[102],"proposed":[105],"method.":[106],"Our":[107],"experiments":[108],"on":[109],"synthetic":[110],"data":[111,114],"real-world":[113],"have":[115],"shown":[116],"that":[117],"our":[118],"significantly":[120],"consistently":[122],"outperforms":[123],"existing":[124],"solutions":[125],"term":[127],"accuracy.":[129]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2019-07-30T00:00:00"}
