{"id":"https://openalex.org/W2027197817","doi":null,"title":"An Interior-Point Method for Large-Scale '1-Regularized Logistic Regression","display_name":"An Interior-Point Method for Large-Scale '1-Regularized Logistic Regression","publication_year":2007,"publication_date":"2007-01-01","ids":{"openalex":"https://openalex.org/W2027197817","mag":"2027197817"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.88.5451","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.88.5451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.csail.mit.edu/papers/volume8/koh07a/koh07a.pdf","raw_type":"text"},"volume":null,"type":"article","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/A5109249218","display_name":"Kwangmoo Koh","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kwangmoo Koh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026356137","display_name":"Seung-Jean Kim","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seung-Jean Kim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5011176205","display_name":"Stephen Boyd","orcid":"https://orcid.org/0000-0001-8353-6000","observed_orcids":["https://orcid.org/0000-0001-8353-6000"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stephen Boyd","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-8353-6000","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":34.2755,"has_fulltext":false,"cited_by_count":549,"citation_normalized_percentile":{"value":0.99762339,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":100},"biblio":{"volume":"8","issue":"54","first_page":"1519","last_page":"1555"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9301999807357788,"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":0.9301999807357788,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.03819999843835831,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.004900000058114529,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/l1-regularized-logistic-regression","display_name":"L1-regularized logistic regression","score":0.8939999938011169},{"id":"https://openalex.org/keywords/interior-point-methods","display_name":"interior point methods","score":0.9760000109672546},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.875},{"id":"https://openalex.org/keywords/large-scale-optimization","display_name":"large-scale optimization","score":0.8809999823570251},{"id":"https://openalex.org/keywords/preconditioned-conjugate-gradient-method","display_name":"preconditioned conjugate gradient","score":0.9700000286102295},{"id":"https://openalex.org/keywords/regularization-path","display_name":"regularization path","score":0.9520000219345093},{"id":"https://openalex.org/keywords/data-sparsity","display_name":"data sparsity","score":0.652999997138977},{"id":"https://openalex.org/keywords/classification","display_name":"classification","score":0.7329999804496765}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.7837212085723877},{"id":"https://openalex.org/C81184566","wikidata":"https://www.wikidata.org/wiki/Q1191895","display_name":"Conjugate gradient method","level":2,"score":0.6677809953689575},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6381632089614868},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.5779990553855896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.533710777759552},{"id":"https://openalex.org/C155253501","wikidata":"https://www.wikidata.org/wiki/Q461992","display_name":"Interior point method","level":2,"score":0.5034417510032654},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.48013922572135925},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46711692214012146},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4610888361930847},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4231339693069458},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.41840216517448425},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4112321138381958},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31285467743873596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22042259573936462},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.19339361786842346},{"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},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.88.5451","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.88.5451","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://jmlr.csail.mit.edu/papers/volume8/koh07a/koh07a.pdf","raw_type":"text"},{"id":"mag:2164078599","doi":null,"is_oa":false,"landing_page_url":"http://jmlr.csail.mit.edu/papers/volume8/koh07a/koh07a.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null},{"id":"mag:2027197817","doi":null,"is_oa":false,"landing_page_url":"https://www.jmlr.org/papers/volume8/koh07a/koh07a.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","ccf-a","cwts-core","dongbi-a","jufo-3","ki-jl-2","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Machine Learning Research","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":84,"referenced_works":["https://openalex.org/W128209428","https://openalex.org/W182881619","https://openalex.org/W191129667","https://openalex.org/W1480376833","https://openalex.org/W1493526108","https://openalex.org/W1496451467","https://openalex.org/W1509805685","https://openalex.org/W1518039036","https://openalex.org/W1528905581","https://openalex.org/W1542491098","https://openalex.org/W1568288633","https://openalex.org/W1580788756","https://openalex.org/W1760551737","https://openalex.org/W1887132526","https://openalex.org/W1975900269","https://openalex.org/W1981885118","https://openalex.org/W1983794113","https://openalex.org/W1994722534","https://openalex.org/W1995691260","https://openalex.org/W2004915807","https://openalex.org/W2015263936","https://openalex.org/W2020925091","https://openalex.org/W2045676435","https://openalex.org/W2047316561","https://openalex.org/W2056636001","https://openalex.org/W2057682568","https://openalex.org/W2058247645","https://openalex.org/W2062102668","https://openalex.org/W2063978378","https://openalex.org/W2078204800","https://openalex.org/W2083940610","https://openalex.org/W2084812512","https://openalex.org/W2085842701","https://openalex.org/W2087684630","https://openalex.org/W2100032953","https://openalex.org/W2101095383","https://openalex.org/W2107275319","https://openalex.org/W2109363337","https://openalex.org/W2116798985","https://openalex.org/W2119073761","https://openalex.org/W2129131372","https://openalex.org/W2129191766","https://openalex.org/W2130698119","https://openalex.org/W2133958955","https://openalex.org/W2135046866","https://openalex.org/W2138019504","https://openalex.org/W2138145347","https://openalex.org/W2140514146","https://openalex.org/W2143628714","https://openalex.org/W2144011735","https://openalex.org/W2145096794","https://openalex.org/W2145473366","https://openalex.org/W2147656689","https://openalex.org/W2149199519","https://openalex.org/W2150579376","https://openalex.org/W2150940164","https://openalex.org/W2152734820","https://openalex.org/W2153597269","https://openalex.org/W2153699776","https://openalex.org/W2154332973","https://openalex.org/W2155681181","https://openalex.org/W2156459512","https://openalex.org/W2164452299","https://openalex.org/W2165154777","https://openalex.org/W2166183437","https://openalex.org/W2168204766","https://openalex.org/W2168334281","https://openalex.org/W2168335598","https://openalex.org/W2170744156","https://openalex.org/W2171048418","https://openalex.org/W2181470043","https://openalex.org/W2266946488","https://openalex.org/W2296319761","https://openalex.org/W2296616510","https://openalex.org/W2338175496","https://openalex.org/W2611293703","https://openalex.org/W2615253071","https://openalex.org/W2949581777","https://openalex.org/W2982720039","https://openalex.org/W3009784374","https://openalex.org/W3015463134","https://openalex.org/W3029645440","https://openalex.org/W4238243512","https://openalex.org/W6959508083"],"related_works":["https://openalex.org/W2164078599","https://openalex.org/W7100795720","https://openalex.org/W138876763","https://openalex.org/W2962018611","https://openalex.org/W2271276396","https://openalex.org/W1982519483","https://openalex.org/W7098177588","https://openalex.org/W2276818649","https://openalex.org/W7099825374","https://openalex.org/W6885032445"],"abstract_inverted_index":{"Logistic":[0],"regression":[1,32],"with":[2,36,57,101],"\u21131":[3],"regularization":[4,129],"has":[5],"been":[6],"proposed":[7],"as":[8],"a":[9,39,52,86,102,114,118,123,140],"promising":[10],"method":[11,26,90],"for":[12,27],"feature":[13],"selection":[14],"in":[15,49,68,75,113],"classification":[16],"problems.":[17,33],"In":[18],"this":[19],"paper":[20],"we":[21],"describe":[22],"an":[23],"efficient":[24],"interior-point":[25],"solving":[28,139],"large-scale":[29],"\u21131-regularized":[30],"logistic":[31],"Small":[34],"problems":[35,143],"up":[37],"to":[38,91],"thousand":[40],"or":[41],"so":[42],"features":[43,62,104],"and":[44,63,105],"examples":[45,106],"can":[46,65,96,131],"be":[47,66,132],"solved":[48,67],"seconds":[50,71],"on":[51,80,117],"PC;":[53],"medium":[54],"sized":[55],"problems,":[56,100],"tens":[58,69],"of":[59,61,70,126,142],"thousands":[60],"examples,":[64],"(assuming":[72],"some":[73],"sparsity":[74],"the":[76,81,93,108,127],"data).":[77],"A":[78],"variation":[79],"basic":[82],"method,":[83],"that":[84],"uses":[85],"preconditioned":[87],"conjugate":[88],"gradient":[89],"compute":[92],"search":[94],"step,":[95],"solve":[97],"very":[98],"large":[99],"million":[103],"(e.g.,":[107],"20":[109],"Newsgroups":[110],"data":[111],"set),":[112],"few":[115],"minutes,":[116],"PC.":[119],"Using":[120],"warm-start":[121],"techniques,":[122],"good":[124],"approximation":[125],"entire":[128],"path":[130],"computed":[133],"much":[134],"more":[135],"efficiently":[136],"than":[137],"by":[138],"family":[141],"independently.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":31},{"year":2020,"cited_by_count":24},{"year":2019,"cited_by_count":34},{"year":2018,"cited_by_count":28},{"year":2017,"cited_by_count":37},{"year":2016,"cited_by_count":44},{"year":2015,"cited_by_count":64},{"year":2014,"cited_by_count":50},{"year":2013,"cited_by_count":38},{"year":2012,"cited_by_count":35}],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2016-06-24T00:00:00"}