{"id":"https://openalex.org/W4304480949","doi":"https://doi.org/10.1109/tpami.2022.3213716","title":"Structured Sparsity Optimization with Non-Convex Surrogates of $\\ell _{2,0}$-Norm: A Unified Algorithmic Framework","display_name":"Structured Sparsity Optimization with Non-Convex Surrogates of $\\ell _{2,0}$-Norm: A Unified Algorithmic Framework","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4304480949","doi":"https://doi.org/10.1109/tpami.2022.3213716","pmid":"https://pubmed.ncbi.nlm.nih.gov/36219668"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2022.3213716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3213716","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","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/A5100699785","display_name":"Xiaoqin Zhang","orcid":"https://orcid.org/0000-0003-0958-7285"},"institutions":[{"id":"https://openalex.org/I146620803","display_name":"Wenzhou University","ror":"https://ror.org/020hxh324","country_code":"CN","type":"education","lineage":["https://openalex.org/I146620803"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqin Zhang","raw_affiliation_strings":["College of Computer Science and Artificial Intelligence, Wenzhou University, China"],"raw_orcid":"https://orcid.org/0000-0003-0958-7285","affiliations":[{"raw_affiliation_string":"College of Computer Science and Artificial Intelligence, Wenzhou University, China","institution_ids":["https://openalex.org/I146620803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072742668","display_name":"Jingjing Zheng","orcid":"https://orcid.org/0000-0003-1955-5308"},"institutions":[{"id":"https://openalex.org/I146620803","display_name":"Wenzhou University","ror":"https://ror.org/020hxh324","country_code":"CN","type":"education","lineage":["https://openalex.org/I146620803"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingjing Zheng","raw_affiliation_strings":["College of Computer Science and Artificial Intelligence, Wenzhou University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Artificial Intelligence, Wenzhou University, China","institution_ids":["https://openalex.org/I146620803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100401463","display_name":"Di Wang","orcid":"https://orcid.org/0000-0003-0435-0609"},"institutions":[{"id":"https://openalex.org/I87445476","display_name":"Xi'an Jiaotong University","ror":"https://ror.org/017zhmm22","country_code":"CN","type":"education","lineage":["https://openalex.org/I87445476"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Di Wang","raw_affiliation_strings":["Center for Intelligent Decision-Making and Machine Learning, School of Management, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China"],"raw_orcid":"https://orcid.org/0000-0003-0435-0609","affiliations":[{"raw_affiliation_string":"Center for Intelligent Decision-Making and Machine Learning, School of Management, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China","institution_ids":["https://openalex.org/I87445476"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055726507","display_name":"Guiying Tang","orcid":"https://orcid.org/0009-0001-4758-6673"},"institutions":[{"id":"https://openalex.org/I146620803","display_name":"Wenzhou University","ror":"https://ror.org/020hxh324","country_code":"CN","type":"education","lineage":["https://openalex.org/I146620803"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guiying Tang","raw_affiliation_strings":["College of Computer Science and Artificial Intelligence, Wenzhou University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Computer Science and Artificial Intelligence, Wenzhou University, China","institution_ids":["https://openalex.org/I146620803"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088606320","display_name":"Zhengyuan Zhou","orcid":"https://orcid.org/0000-0002-0005-9411"},"institutions":[{"id":"https://openalex.org/I97018004","display_name":"Stanford University","ror":"https://ror.org/00f54p054","country_code":"US","type":"education","lineage":["https://openalex.org/I97018004"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhengyuan Zhou","raw_affiliation_strings":["Department of Electrical Engineering, Stanford University, Stanford, USA"],"raw_orcid":"https://orcid.org/0000-0002-0005-9411","affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Stanford University, Stanford, USA","institution_ids":["https://openalex.org/I97018004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016399094","display_name":"Zhouchen Lin","orcid":"https://orcid.org/0000-0003-1493-7569"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhouchen Lin","raw_affiliation_strings":["Key Laboratory of Machine Perception (MOE), School of Intelligence Science and Technology, Peking University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1493-7569","affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Perception (MOE), School of Intelligence Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7946,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.9146716,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"45","issue":"5","first_page":"1","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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":1.0,"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":0.9940000176429749,"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9919000267982483,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.608159601688385},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.6039288640022278},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5912485122680664},{"id":"https://openalex.org/keywords/convex-optimization","display_name":"Convex optimization","score":0.5725582242012024},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.5579732656478882},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.5521709322929382},{"id":"https://openalex.org/keywords/convex-function","display_name":"Convex function","score":0.47142285108566284},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.43235689401626587},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.4125530421733856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37372270226478577},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3388257622718811},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2960510849952698}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.608159601688385},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.6039288640022278},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5912485122680664},{"id":"https://openalex.org/C157972887","wikidata":"https://www.wikidata.org/wiki/Q463359","display_name":"Convex optimization","level":3,"score":0.5725582242012024},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.5579732656478882},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.5521709322929382},{"id":"https://openalex.org/C145446738","wikidata":"https://www.wikidata.org/wiki/Q319913","display_name":"Convex function","level":3,"score":0.47142285108566284},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.43235689401626587},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.4125530421733856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37372270226478577},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3388257622718811},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2960510849952698},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2022.3213716","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2022.3213716","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:36219668","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36219668","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 pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":73,"referenced_works":["https://openalex.org/W1565746575","https://openalex.org/W1583700199","https://openalex.org/W1860736741","https://openalex.org/W1876688746","https://openalex.org/W1980500788","https://openalex.org/W1987572733","https://openalex.org/W1993962865","https://openalex.org/W1995168330","https://openalex.org/W1996726072","https://openalex.org/W1997201895","https://openalex.org/W2009596443","https://openalex.org/W2043317267","https://openalex.org/W2046769852","https://openalex.org/W2052311585","https://openalex.org/W2061572659","https://openalex.org/W2069959554","https://openalex.org/W2072356364","https://openalex.org/W2075547019","https://openalex.org/W2082855665","https://openalex.org/W2087018183","https://openalex.org/W2093753603","https://openalex.org/W2100346396","https://openalex.org/W2111854674","https://openalex.org/W2114122776","https://openalex.org/W2114147096","https://openalex.org/W2118550318","https://openalex.org/W2129812935","https://openalex.org/W2142040002","https://openalex.org/W2144059227","https://openalex.org/W2145962650","https://openalex.org/W2154053567","https://openalex.org/W2160396672","https://openalex.org/W2160547390","https://openalex.org/W2163376981","https://openalex.org/W2164452299","https://openalex.org/W2165916500","https://openalex.org/W2167577940","https://openalex.org/W2170858806","https://openalex.org/W2199944189","https://openalex.org/W2267569616","https://openalex.org/W2441198140","https://openalex.org/W2528144111","https://openalex.org/W2540507728","https://openalex.org/W2558981930","https://openalex.org/W2611328865","https://openalex.org/W2740959330","https://openalex.org/W2779692282","https://openalex.org/W2792024885","https://openalex.org/W2794268568","https://openalex.org/W2800991716","https://openalex.org/W2885952768","https://openalex.org/W2886784716","https://openalex.org/W2894142921","https://openalex.org/W2897362204","https://openalex.org/W2897861496","https://openalex.org/W2902808875","https://openalex.org/W2955631806","https://openalex.org/W2960871125","https://openalex.org/W2963218026","https://openalex.org/W2970995493","https://openalex.org/W2971348295","https://openalex.org/W2984404693","https://openalex.org/W3007731094","https://openalex.org/W3023913369","https://openalex.org/W3102415113","https://openalex.org/W4234903972","https://openalex.org/W4240385847","https://openalex.org/W4250739957","https://openalex.org/W4255694139","https://openalex.org/W6603183647","https://openalex.org/W6633774736","https://openalex.org/W6682433143","https://openalex.org/W6767820044"],"related_works":["https://openalex.org/W3035814349","https://openalex.org/W2518949622","https://openalex.org/W2187449906","https://openalex.org/W4285101096","https://openalex.org/W4320477335","https://openalex.org/W1561889708","https://openalex.org/W4382725876","https://openalex.org/W3011190762","https://openalex.org/W2275184629","https://openalex.org/W2115614142"],"abstract_inverted_index":{"In":[0,143],"this":[1,61],"paper,":[2],"we":[3,63,89,118],"present":[4],"a":[5,46,55,66,71,91,97,110,158],"general":[6],"optimization":[7,104,141,148],"framework":[8,67],"that":[9,68,95],"leverages":[10],"structured":[11,24,86,101,130,139],"sparsity":[12,140],"to":[13,36,106],"achieve":[14],"superior":[15],"recovery":[16,103],"results.":[17],"The":[18],"traditional":[19],"method":[20],"for":[21,70],"solving":[22],"the":[23,38,137,147,178,183],"sparse":[25,102],"objectives":[26],"based":[27],"on":[28,116,170],"<inline-formula":[29,39],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[30,40],"xmlns:xlink=\"http://www.w3.org/1999/xlink\"><tex-math":[31,41],"notation=\"LaTeX\">$\\ell":[32,42],"_{2,0}$</tex-math></inline-formula>":[33],"-norm":[34,44],"is":[35,162],"use":[37],"_{2,1}$</tex-math></inline-formula>":[43],"as":[45],"convex":[47],"surrogate.":[48],"However,":[49],"such":[50],"an":[51],"approximation":[52],"often":[53],"yields":[54],"large":[56],"performance":[57,83],"gap.":[58],"To":[59],"tackle":[60],"issue,":[62],"first":[64],"provide":[65],"allows":[69],"wide":[72],"range":[73],"of":[74,182],"surrogate":[75,160],"functions":[76],"(including":[77],"non-convex":[78,100],"surrogates),":[79],"which":[80,133],"exhibits":[81],"better":[82],"in":[84,164],"harnessing":[85],"sparsity.":[87],"Moreover,":[88],"develop":[90],"fixed":[92],"point":[93],"algorithm":[94],"solves":[96],"key":[98],"underlying":[99],"problem":[105,149],"global":[107],"optimality":[108],"with":[109],"guaranteed":[111],"super-linear":[112],"convergence":[113],"rate.":[114],"Building":[115],"this,":[117],"consider":[119],"three":[120],"specific":[121],"applications,":[122],"i.e.,":[123],"outlier":[124],"pursuit,":[125],"supervised":[126],"feature":[127],"selection,":[128],"and":[129,153,173,176,180],"dictionary":[131],"learning,":[132],"can":[134,150],"benefit":[135],"from":[136],"proposed":[138,184],"framework.":[142,185],"each":[144],"application,":[145],"how":[146],"be":[151,155],"formulated":[152],"thus":[154],"relaxed":[156],"under":[157],"generic":[159],"function":[161],"explained":[163],"detail.":[165],"We":[166],"conduct":[167],"extensive":[168],"experiments":[169],"both":[171],"synthetic":[172],"real-world":[174],"data":[175],"demonstrate":[177],"effectiveness":[179],"efficiency":[181]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2022-10-12T00:00:00"}
