{"id":"https://openalex.org/W3195159534","doi":"https://doi.org/10.1109/tnnls.2021.3104840","title":"An Accelerated Maximally Split ADMM for a Class of Generalized Ridge Regression","display_name":"An Accelerated Maximally Split ADMM for a Class of Generalized Ridge Regression","publication_year":2021,"publication_date":"2021-08-26","ids":{"openalex":"https://openalex.org/W3195159534","doi":"https://doi.org/10.1109/tnnls.2021.3104840","mag":"3195159534","pmid":"https://pubmed.ncbi.nlm.nih.gov/34437070"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2021.3104840","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3104840","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","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/A5002975495","display_name":"Xiaoping Lai","orcid":"https://orcid.org/0000-0002-5878-5563"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoping Lai","raw_affiliation_strings":["Institute of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, China","Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-5878-5563","affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]},{"raw_affiliation_string":"Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074063024","display_name":"Jiuwen Cao","orcid":"https://orcid.org/0000-0002-6480-5794"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiuwen Cao","raw_affiliation_strings":["Institute of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, China","Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou Dianzi University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-6480-5794","affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]},{"raw_affiliation_string":"Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049506273","display_name":"Zhiping Lin","orcid":"https://orcid.org/0000-0002-1587-1226"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zhiping Lin","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-1587-1226","affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6824,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.75400857,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"34","issue":"2","first_page":"958","last_page":"972"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T12676","display_name":"Machine Learning and ELM","score":0.9998000264167786,"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":0.9984999895095825,"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/T10320","display_name":"Neural Networks and Applications","score":0.9958000183105469,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.791981041431427},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5833303928375244},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5767456889152527},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5513612031936646},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5068607926368713},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4959343373775482},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42178788781166077},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.371229887008667},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36949414014816284},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3373975455760956},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13171148300170898}],"concepts":[{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.791981041431427},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5833303928375244},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5767456889152527},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5513612031936646},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5068607926368713},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4959343373775482},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42178788781166077},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.371229887008667},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36949414014816284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3373975455760956},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13171148300170898},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2021.3104840","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2021.3104840","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"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":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:34437070","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/34437070","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 neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7151297541","display_name":null,"funder_award_id":"U1909209","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":70,"referenced_works":["https://openalex.org/W106686038","https://openalex.org/W1246301972","https://openalex.org/W1409984952","https://openalex.org/W1540550726","https://openalex.org/W1549918636","https://openalex.org/W1589558030","https://openalex.org/W1604569548","https://openalex.org/W1757980361","https://openalex.org/W1964927113","https://openalex.org/W1972552174","https://openalex.org/W1972977357","https://openalex.org/W2001362863","https://openalex.org/W2003423725","https://openalex.org/W2026131661","https://openalex.org/W2027650277","https://openalex.org/W2037603831","https://openalex.org/W2047028564","https://openalex.org/W2051245758","https://openalex.org/W2052842899","https://openalex.org/W2058532290","https://openalex.org/W2076261573","https://openalex.org/W2076443308","https://openalex.org/W2092620240","https://openalex.org/W2105693192","https://openalex.org/W2111076441","https://openalex.org/W2124541940","https://openalex.org/W2129249398","https://openalex.org/W2137234026","https://openalex.org/W2155723734","https://openalex.org/W2158547433","https://openalex.org/W2161078014","https://openalex.org/W2164278908","https://openalex.org/W2208792218","https://openalex.org/W2244987555","https://openalex.org/W2277973662","https://openalex.org/W2296319761","https://openalex.org/W2413539728","https://openalex.org/W2418562807","https://openalex.org/W2463468632","https://openalex.org/W2496350169","https://openalex.org/W2502737401","https://openalex.org/W2526749264","https://openalex.org/W2552316025","https://openalex.org/W2622612680","https://openalex.org/W2738226240","https://openalex.org/W2749412917","https://openalex.org/W2787894218","https://openalex.org/W2801613683","https://openalex.org/W2898430976","https://openalex.org/W2901276090","https://openalex.org/W2909570363","https://openalex.org/W2911755921","https://openalex.org/W2914205480","https://openalex.org/W2964056799","https://openalex.org/W2964853047","https://openalex.org/W2968901045","https://openalex.org/W2973452494","https://openalex.org/W3044178239","https://openalex.org/W3096860278","https://openalex.org/W3099831354","https://openalex.org/W3113987434","https://openalex.org/W3123607434","https://openalex.org/W3173138228","https://openalex.org/W4213251304","https://openalex.org/W4243251406","https://openalex.org/W4245876913","https://openalex.org/W4250589301","https://openalex.org/W4256202811","https://openalex.org/W4291236916","https://openalex.org/W6784179662"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175","https://openalex.org/W4230315250"],"abstract_inverted_index":{"Ridge":[0],"regression":[1,56,130],"(RR)":[2],"has":[3],"been":[4],"commonly":[5],"used":[6],"in":[7,15,112],"machine":[8],"learning,":[9],"but":[10],"is":[11,69,103,109],"facing":[12],"computational":[13,143],"challenges":[14],"big":[16],"data":[17],"applications.":[18],"To":[19],"meet":[20],"the":[21,61,74,77,93,97,113,139,149],"challenges,":[22],"this":[23],"article":[24],"develops":[25],"a":[26,43,80,89],"highly":[27],"parallel":[28],"new":[29,62,107,150],"algorithm,":[30],"i.e.,":[31],"an":[32],"accelerated":[33],"maximally":[34],"split":[35],"alternating":[36],"direction":[37],"method":[38],"of":[39,45,60,73,83,92,115,148],"multipliers":[40],"(A-MS-ADMM),":[41],"for":[42,54,76,96,129],"class":[44],"generalized":[46],"RR":[47],"(GRR)":[48],"that":[49],"allows":[50],"different":[51,55],"regularization":[52,84,101],"factors":[53,85,102],"coefficients.":[57],"Linear":[58],"convergence":[59,67],"algorithm":[63,75,94,108],"along":[64],"with":[65,79,99,135],"its":[66],"ratio":[68],"established.":[70],"Optimal":[71],"parameters":[72,95],"GRR":[78,98],"particular":[81],"set":[82],"are":[86],"derived,":[87],"and":[88,131,133,145],"selection":[90],"scheme":[91],"general":[100],"also":[104],"discussed.":[105],"The":[106],"then":[110],"applied":[111],"training":[114],"single-layer":[116],"feedforward":[117],"neural":[118],"networks.":[119],"Experimental":[120],"results":[121],"on":[122,125],"performance":[123],"validation":[124],"real-world":[126],"benchmark":[127],"datasets":[128],"classification":[132],"comparisons":[134],"existing":[136],"methods":[137],"demonstrate":[138],"fast":[140],"convergence,":[141],"low":[142],"complexity,":[144],"high":[146],"parallelism":[147],"algorithm.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}