{"id":"https://openalex.org/W2890622213","doi":"https://doi.org/10.1080/00401706.2018.1497544","title":"Convex Bidirectional Large Margin Classifiers","display_name":"Convex Bidirectional Large Margin Classifiers","publication_year":2018,"publication_date":"2018-09-12","ids":{"openalex":"https://openalex.org/W2890622213","doi":"https://doi.org/10.1080/00401706.2018.1497544","mag":"2890622213","pmid":"https://pubmed.ncbi.nlm.nih.gov/31485090"},"language":"en","primary_location":{"id":"doi:10.1080/00401706.2018.1497544","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2018.1497544","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"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":"Technometrics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6726400","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078483423","display_name":"Zhengling Qi","orcid":"https://orcid.org/0000-0003-0270-7969"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhengling Qi","raw_affiliation_strings":["Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC","institution_ids":["https://openalex.org/I114027177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100376614","display_name":"Yufeng Liu","orcid":"https://orcid.org/0000-0002-1686-0545"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]},{"id":"https://openalex.org/I4390039329","display_name":"UNC Lineberger Comprehensive Cancer Center","ror":"https://ror.org/043ehm030","country_code":null,"type":"healthcare","lineage":["https://openalex.org/I114027177","https://openalex.org/I4390039329"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yufeng Liu","raw_affiliation_strings":["Department of Genetics, Department of Biostatistics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC","Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Genetics, Department of Biostatistics, Carolina Center for Genome Sciences, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC","institution_ids":["https://openalex.org/I114027177","https://openalex.org/I4390039329"]},{"raw_affiliation_string":"Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09609538,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"61","issue":"2","first_page":"176","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":0.9961000084877014,"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":0.9961000084877014,"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":0.994700014591217,"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/T10320","display_name":"Neural Networks and Applications","score":0.9944000244140625,"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/interpretability","display_name":"Interpretability","score":0.90335613489151},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6084641218185425},{"id":"https://openalex.org/keywords/bilinear-interpolation","display_name":"Bilinear interpolation","score":0.60674649477005},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6060561537742615},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5982745885848999},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5903027653694153},{"id":"https://openalex.org/keywords/random-subspace-method","display_name":"Random subspace method","score":0.536931574344635},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5164685249328613},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47464069724082947},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4566537141799927},{"id":"https://openalex.org/keywords/regular-polygon","display_name":"Regular polygon","score":0.4535381495952606},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.43668562173843384},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4329940974712372},{"id":"https://openalex.org/keywords/linear-classifier","display_name":"Linear classifier","score":0.42078667879104614},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4082431197166443},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27485308051109314},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.07381132245063782}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.90335613489151},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6084641218185425},{"id":"https://openalex.org/C205203396","wikidata":"https://www.wikidata.org/wiki/Q612143","display_name":"Bilinear interpolation","level":2,"score":0.60674649477005},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6060561537742615},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5982745885848999},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5903027653694153},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.536931574344635},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5164685249328613},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47464069724082947},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4566537141799927},{"id":"https://openalex.org/C112680207","wikidata":"https://www.wikidata.org/wiki/Q714886","display_name":"Regular polygon","level":2,"score":0.4535381495952606},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.43668562173843384},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4329940974712372},{"id":"https://openalex.org/C139532973","wikidata":"https://www.wikidata.org/wiki/Q2679259","display_name":"Linear classifier","level":3,"score":0.42078667879104614},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4082431197166443},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27485308051109314},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.07381132245063782},{"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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":3,"locations":[{"id":"doi:10.1080/00401706.2018.1497544","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2018.1497544","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"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":"Technometrics","raw_type":"journal-article"},{"id":"pmid:31485090","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31485090","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":"Technometrics : a journal of statistics for the physical, chemical, and engineering sciences","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:6726400","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6726400","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Technometrics","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:6726400","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6726400","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Technometrics","raw_type":"Text"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.4399999976158142,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1209886030","display_name":null,"funder_award_id":"R01 GM126550","funder_id":"https://openalex.org/F4320337354","funder_display_name":"National Institute of General Medical Sciences"}],"funders":[{"id":"https://openalex.org/F4320337354","display_name":"National Institute of General Medical Sciences","ror":"https://ror.org/04q48ey07"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1563653928","https://openalex.org/W1618449801","https://openalex.org/W1749992802","https://openalex.org/W1975846642","https://openalex.org/W1979089199","https://openalex.org/W1988790447","https://openalex.org/W1998894275","https://openalex.org/W2007527993","https://openalex.org/W2048527384","https://openalex.org/W2050968963","https://openalex.org/W2057682568","https://openalex.org/W2080726496","https://openalex.org/W2086623929","https://openalex.org/W2100556411","https://openalex.org/W2103972604","https://openalex.org/W2130698119","https://openalex.org/W2134332047","https://openalex.org/W2135046866","https://openalex.org/W2135293965","https://openalex.org/W2138550913","https://openalex.org/W2142712398","https://openalex.org/W2145073242","https://openalex.org/W2146130798","https://openalex.org/W2148603752","https://openalex.org/W2149298154","https://openalex.org/W2249156281","https://openalex.org/W2604272474","https://openalex.org/W2612972698","https://openalex.org/W3105120000","https://openalex.org/W4244393449"],"related_works":["https://openalex.org/W2380748039","https://openalex.org/W2371425310","https://openalex.org/W2998259334","https://openalex.org/W2239966463","https://openalex.org/W2408521075","https://openalex.org/W2641936149","https://openalex.org/W4298201649","https://openalex.org/W2903240209","https://openalex.org/W2462617003","https://openalex.org/W3102620718"],"abstract_inverted_index":{"Classification":[0],"problems":[1],"are":[2,113],"commonly":[3],"seen":[4],"in":[5,96,116],"practice.":[6],"In":[7],"this":[8],"paper,":[9],"we":[10],"aim":[11],"to":[12,41,82,104],"develop":[13],"classifiers":[14,40,50],"that":[15],"can":[16],"enjoy":[17],"great":[18],"interpretability":[19,110],"as":[20,31,128],"linear":[21,46],"classifiers,":[22],"and":[23,47,93,109],"at":[24],"the":[25,43,75,117,123],"same":[26],"time":[27],"have":[28,105],"model":[29],"flexibility":[30],"nonlinear":[32,49],"classifiers.":[33],"We":[34],"propose":[35],"convex":[36],"bidirectional":[37],"large":[38],"margin":[39],"fill":[42],"gap":[44],"between":[45],"general":[48],"for":[51,63],"high":[52,66,97],"dimensional":[53,67,98],"data.":[54,68,118],"Our":[55],"method":[56,101,125],"provides":[57],"a":[58],"new":[59],"data":[60,95],"visualization":[61],"tool":[62],"classification":[64],"of":[65,91,122],"The":[69,119],"obtained":[70],"bilinear":[71],"projection":[72],"structure":[73],"makes":[74],"proposed":[76,124],"classifier":[77],"very":[78],"interpretable.":[79],"Additional":[80],"shrinkage":[81],"approximate":[83],"variable":[84],"selection":[85],"is":[86,102,126],"also":[87],"considered.":[88],"Through":[89],"analysis":[90],"simulated":[92],"real":[94],"settings,":[99],"our":[100],"shown":[103],"superior":[106],"prediction":[107],"performance":[108],"when":[111],"there":[112],"potential":[114],"subpopulations":[115],"computer":[120],"code":[121],"available":[127],"supplemental":[129],"materials.":[130]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}