{"id":"https://openalex.org/W605165147","doi":null,"title":"Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems","display_name":"Safe Subspace Screening for Nuclear Norm Regularized Least Squares Problems","publication_year":2015,"publication_date":"2015-07-06","ids":{"openalex":"https://openalex.org/W605165147","mag":"605165147"},"language":"en","primary_location":{"id":"mag:605165147","doi":null,"is_oa":false,"landing_page_url":"http://proceedings.mlr.press/v37/zhoua15.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306419644","display_name":"International Conference on Machine Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Machine Learning","raw_type":null},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":[],"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/A5020198396","display_name":"Qiang Zhou","orcid":"https://orcid.org/0009-0008-8662-7694","observed_orcids":["https://orcid.org/0009-0008-8662-7694"]},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Qiang Zhou","raw_affiliation_strings":["Department of Electrical and Computer Engineering; National University of Singapore; Singapore 117583"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering; National University of Singapore; Singapore 117583","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101722127","display_name":"Qi Zhao","orcid":"https://orcid.org/0000-0002-3508-027X","observed_orcids":["https://orcid.org/0000-0002-3508-027X"]},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Qi Zhao","raw_affiliation_strings":["Department of Electrical and Computer Engineering; National University of Singapore; Singapore 117583"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering; National University of Singapore; Singapore 117583","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165932596"],"apc_list":null,"apc_paid":null,"fwci":0.9771,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.67175457,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1103","last_page":"1112"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9800000190734863,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9800000190734863,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.008999999612569809,"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/T10136","display_name":"Statistical Methods and Inference","score":0.005900000222027302,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/nuclear-norm-regularization","display_name":"nuclear norm regularization","score":0.9990000128746033},{"id":"https://openalex.org/keywords/low-rank-matrix-estimation","display_name":"low-rank matrix estimation","score":0.7979999780654907},{"id":"https://openalex.org/keywords/large-scale-optimization","display_name":"large-scale optimization","score":0.6600000262260437},{"id":"https://openalex.org/keywords/problem-size-reduction","display_name":"problem size reduction","score":0.640999972820282}],"concepts":[{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.8071423768997192},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.664789617061615},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.660420298576355},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5640227198600769},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.5627564191818237},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5487105846405029},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.4820745587348938},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4432946741580963},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4375455975532532},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.31422048807144165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.19194623827934265},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.12848147749900818},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:605165147","doi":null,"is_oa":false,"landing_page_url":"http://proceedings.mlr.press/v37/zhoua15.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306419644","display_name":"International Conference on Machine Learning","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Machine Learning","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W135566594","https://openalex.org/W641467550","https://openalex.org/W1543911290","https://openalex.org/W1574851760","https://openalex.org/W1648550497","https://openalex.org/W1762430620","https://openalex.org/W1824659232","https://openalex.org/W1998367754","https://openalex.org/W1998635907","https://openalex.org/W2006793117","https://openalex.org/W2016119924","https://openalex.org/W2029213856","https://openalex.org/W2037549374","https://openalex.org/W2065180801","https://openalex.org/W2097620183","https://openalex.org/W2101399145","https://openalex.org/W2101593431","https://openalex.org/W2103325283","https://openalex.org/W2107654046","https://openalex.org/W2112495313","https://openalex.org/W2112796928","https://openalex.org/W2131060185","https://openalex.org/W2131964154","https://openalex.org/W2135046866","https://openalex.org/W2137475104","https://openalex.org/W2145831204","https://openalex.org/W2146130798","https://openalex.org/W2152726866","https://openalex.org/W2154560360","https://openalex.org/W2164278908","https://openalex.org/W2186054958","https://openalex.org/W2339666411","https://openalex.org/W2949664970","https://openalex.org/W2951269501"],"related_works":["https://openalex.org/W7116083761","https://openalex.org/W2145831204","https://openalex.org/W1591995413","https://openalex.org/W5368005","https://openalex.org/W2013854956","https://openalex.org/W3128647534","https://openalex.org/W1824659232","https://openalex.org/W2085013758","https://openalex.org/W7171656035","https://openalex.org/W68366368"],"abstract_inverted_index":{"Nuclear":[0],"norm":[1,36,83,172],"regularization":[2],"has":[3],"been":[4,23],"shown":[5],"very":[6,196],"promising":[7],"for":[8,29,40,81],"pursing":[9],"a":[10,47,66,101,109,125],"low":[11,94],"rank":[12,95],"matrix":[13,42,59],"solution":[14,96,150,155],"in":[15,34,147,198],"various":[16],"machine":[17],"learning":[18],"problems.":[19,87],"Many":[20],"efforts":[21],"have":[22],"devoted":[24],"to":[25,74,116,120,131,153],"develop":[26],"efficient":[27,136],"algorithms":[28],"solving":[30,117],"the":[31,51,56,76,79,90,93,104,118,139,154,157,161,179,192],"optimization":[32],"problem":[33,119,122,128],"nuclear":[35,82,171],"regularization.":[37],"Solving":[38],"it":[39,134,175],"large-scale":[41],"variables,":[43],"however,":[44],"is":[45,129,145,151,176,195],"still":[46],"challenging":[48],"task":[49],"since":[50,174],"complexity":[52],"grows":[53],"fast":[54],"with":[55,168],"size":[57],"of":[58,78,112,178],"variable.":[60],"In":[61,159],"this":[62],"work,":[63],"we":[64],"propose":[65],"novel":[67],"method":[68,106],"called":[69],"safe":[70],"subspace":[71,200],"screening":[72],"(SSS),":[73],"improve":[75],"efficiency":[77],"solver":[80,173],"regularized":[84],"least":[85],"squares":[86],"Motivated":[88],"by":[89,100],"fact":[91],"that":[92,148,191],"can":[97,164],"be":[98,165],"represented":[99],"few":[102],"subspaces,":[103],"proposed":[105,143,162,193],"accurately":[107],"discards":[108],"predominant":[110],"percentage":[111],"inactive":[113,199],"subspaces":[114],"prior":[115],"reduce":[121],"size.":[123],"Consequently,":[124],"much":[126],"smaller":[127],"required":[130],"solve,":[132],"making":[133],"more":[135],"than":[137],"optimizing":[138],"original":[140],"problem.":[141],"The":[142],"SSS":[144,163,194],"safe,":[146],"its":[149],"identical":[152],"from":[156],"solver.":[158,180],"addition,":[160],"used":[166],"together":[167],"any":[169],"existing":[170],"independent":[177],"Extensive":[181],"results":[182],"on":[183],"several":[184],"synthetic":[185],"and":[186],"real":[187],"data":[188],"sets":[189],"show":[190],"effective":[197],"screening.":[201]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}