{"id":"https://openalex.org/W4399203671","doi":"https://doi.org/10.1080/10618600.2024.2362230","title":"Class-Distributed Learning for Multinomial Logistic Regression with High Dimensional Features and a Large Number of Classes","display_name":"Class-Distributed Learning for Multinomial Logistic Regression with High Dimensional Features and a Large Number of Classes","publication_year":2024,"publication_date":"2024-05-31","ids":{"openalex":"https://openalex.org/W4399203671","doi":"https://doi.org/10.1080/10618600.2024.2362230"},"language":"en","primary_location":{"id":"doi:10.1080/10618600.2024.2362230","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2024.2362230","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"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/A5101910693","display_name":"Shuyuan Wu","orcid":"https://orcid.org/0009-0000-4203-5103"},"institutions":[{"id":"https://openalex.org/I181679659","display_name":"Shanghai University of Finance and Economics","ror":"https://ror.org/00wtvfq62","country_code":"CN","type":"education","lineage":["https://openalex.org/I181679659"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuyuan Wu","raw_affiliation_strings":["School of Statistics and Management, Shanghai University of Finance and Economics","School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"School of Statistics and Management, Shanghai University of Finance and Economics","institution_ids":["https://openalex.org/I181679659"]},{"raw_affiliation_string":"School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China","institution_ids":["https://openalex.org/I181679659"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017375826","display_name":"Jing Zhou","orcid":"https://orcid.org/0009-0009-9397-3824"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jing Zhou","raw_affiliation_strings":["Center for Applied Statistics, School of Statistics, Renmin University of China","Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Center for Applied Statistics, School of Statistics, Renmin University of China","institution_ids":["https://openalex.org/I78988378"]},{"raw_affiliation_string":"Center for Applied Statistics, School of Statistics, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100785753","display_name":"Ke Xu","orcid":"https://orcid.org/0000-0001-7222-9523"},"institutions":[{"id":"https://openalex.org/I146563203","display_name":"University of International Business and Economics","ror":"https://ror.org/05khqpb71","country_code":"CN","type":"education","lineage":["https://openalex.org/I146563203"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Xu","raw_affiliation_strings":["School of Statistics, University of International Business and Economics","School of Statistics, University of International Business and Economics, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Statistics, University of International Business and Economics","institution_ids":["https://openalex.org/I146563203"]},{"raw_affiliation_string":"School of Statistics, University of International Business and Economics, Beijing, China","institution_ids":["https://openalex.org/I146563203"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048394694","display_name":"Hansheng Wang","orcid":"https://orcid.org/0000-0003-2386-0209"},"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":"Hansheng Wang","raw_affiliation_strings":["Guanghua School of Management, Peking University","Guanghua School of Management, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Guanghua School of Management, Peking University","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Guanghua School of Management, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5017375826"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0683106,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":"1","first_page":"175","last_page":"186"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9941999912261963,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9941999912261963,"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"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.991599977016449,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9864000082015991,"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/multinomial-logistic-regression","display_name":"Multinomial logistic regression","score":0.9128051400184631},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.6938136219978333},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.6274352669715881},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5430843234062195},{"id":"https://openalex.org/keywords/multinomial-distribution","display_name":"Multinomial distribution","score":0.5300851464271545},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5032023787498474},{"id":"https://openalex.org/keywords/logistic-model-tree","display_name":"Logistic model tree","score":0.49088674783706665},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4649777412414551},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3729393482208252},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3543756604194641},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3348381519317627}],"concepts":[{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.9128051400184631},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.6938136219978333},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.6274352669715881},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5430843234062195},{"id":"https://openalex.org/C192065140","wikidata":"https://www.wikidata.org/wiki/Q1147928","display_name":"Multinomial distribution","level":2,"score":0.5300851464271545},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5032023787498474},{"id":"https://openalex.org/C61722155","wikidata":"https://www.wikidata.org/wiki/Q6667643","display_name":"Logistic model tree","level":3,"score":0.49088674783706665},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4649777412414551},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3729393482208252},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3543756604194641},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3348381519317627}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/10618600.2024.2362230","is_oa":false,"landing_page_url":"https://doi.org/10.1080/10618600.2024.2362230","pdf_url":null,"source":{"id":"https://openalex.org/S76159266","display_name":"Journal of Computational and Graphical Statistics","issn_l":"1061-8600","issn":["1061-8600","1537-2715"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computational and Graphical Statistics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3699622772","display_name":null,"funder_award_id":"12271012","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W34176136","https://openalex.org/W573643533","https://openalex.org/W642783518","https://openalex.org/W1807984730","https://openalex.org/W1978156694","https://openalex.org/W1979158802","https://openalex.org/W1984280847","https://openalex.org/W1995738794","https://openalex.org/W2011756661","https://openalex.org/W2014610539","https://openalex.org/W2043024694","https://openalex.org/W2049496090","https://openalex.org/W2074476634","https://openalex.org/W2078831169","https://openalex.org/W2081566336","https://openalex.org/W2094607565","https://openalex.org/W2108475251","https://openalex.org/W2108598243","https://openalex.org/W2134910934","https://openalex.org/W2135086139","https://openalex.org/W2141062690","https://openalex.org/W2141635270","https://openalex.org/W2146194630","https://openalex.org/W2154560360","https://openalex.org/W2155423555","https://openalex.org/W2163490846","https://openalex.org/W2163605009","https://openalex.org/W2164092415","https://openalex.org/W2165533158","https://openalex.org/W2171050905","https://openalex.org/W2172272342","https://openalex.org/W2314467091","https://openalex.org/W2318588002","https://openalex.org/W2487770199","https://openalex.org/W2616138645","https://openalex.org/W2622263826","https://openalex.org/W2801490189","https://openalex.org/W2801571965","https://openalex.org/W2803867449","https://openalex.org/W2950126918","https://openalex.org/W2953101345","https://openalex.org/W2962696932","https://openalex.org/W2964231067","https://openalex.org/W2967703916","https://openalex.org/W2982674132","https://openalex.org/W3017435008","https://openalex.org/W3098364281","https://openalex.org/W3101440325","https://openalex.org/W3103200295","https://openalex.org/W3122178467","https://openalex.org/W3124968907","https://openalex.org/W3158182398","https://openalex.org/W3200352899","https://openalex.org/W4213170682","https://openalex.org/W4238805501","https://openalex.org/W4299689471","https://openalex.org/W4301960582"],"related_works":["https://openalex.org/W2104977651","https://openalex.org/W2369306031","https://openalex.org/W2059099031","https://openalex.org/W2418252711","https://openalex.org/W3098841390","https://openalex.org/W4367335967","https://openalex.org/W4225160365","https://openalex.org/W4248368593","https://openalex.org/W4246416652","https://openalex.org/W3033697969"],"abstract_inverted_index":{"Estimating":[0],"a":[1,8,42,59,65,94,119],"high-dimensional":[2],"multinomial":[3],"logistic":[4],"regression":[5],"model":[6,114],"with":[7,64],"larger":[9],"number":[10],"of":[11,14,49,104,118],"categories":[12],"is":[13,48,73],"fundamental":[15],"importance":[16],"but":[17],"it":[18,23,30],"presents":[19],"two":[20,76,87],"challenges.":[21],"Computationally,":[22],"leads":[24],"to":[25,37,107],"heavy":[26],"computation":[27],"cost.":[28],"Statistically,":[29],"suffers":[31],"unsatisfactory":[32],"statistical":[33,84],"efficiency.":[34,85],"Therefore,":[35],"how":[36],"solve":[38],"this":[39],"problem":[40],"in":[41],"computationally":[43],"and":[44,82,93,135],"statistically":[45],"efficient":[46],"way":[47],"great":[50],"interest.":[51],"To":[52,125],"tackle":[53],"these":[54],"challenges,":[55],"we":[56,130],"have":[57],"developed":[58],"new":[60,128],"class-distributed":[61,120],"learning":[62],"algorithm":[63,121],"rank-reducible":[66],"coefficient":[67],"structure.":[68],"The":[69,86],"key":[70],"innovation":[71],"here":[72],"piecing":[74],"together":[75],"important":[77],"techniques":[78,88],"for":[79,122],"distributed":[80,123],"computing":[81],"improved":[83],"are,":[89],"respectively,":[90],"dimension":[91],"reduction":[92,99],"circular-structured":[95,112],"working":[96,113],"model.":[97],"Dimension":[98],"effectively":[100],"alleviates":[101],"the":[102,116],"curse":[103],"dimensionality":[105],"due":[106],"high":[108],"dimensional":[109],"features.":[110],"A":[111],"allows":[115],"use":[117],"computing.":[124],"support":[126],"our":[127],"methodology,":[129],"develop":[131],"rigorous":[132],"asymptotic":[133],"theory":[134],"present":[136],"extensive":[137],"numerical":[138],"experiments.":[139]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
