{"id":"https://openalex.org/W2015130450","doi":"https://doi.org/10.1109/globalsip.2013.6737094","title":"Equity factor analysis via column subset selection","display_name":"Equity factor analysis via column subset selection","publication_year":2013,"publication_date":"2013-12-01","ids":{"openalex":"https://openalex.org/W2015130450","doi":"https://doi.org/10.1109/globalsip.2013.6737094","mag":"2015130450"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2013.6737094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2013.6737094","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Global Conference on Signal and Information Processing","raw_type":"proceedings-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/A5010385482","display_name":"Christos Boutsidis","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Christos Boutsidis","raw_affiliation_strings":["Business Analytics and Mathematical Sciences Department, IBM Research Yorktown Heights, NY, USA","Bus. Analytics & Math. Sci. Dept., IBM Res., Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Business Analytics and Mathematical Sciences Department, IBM Research Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]},{"raw_affiliation_string":"Bus. Analytics & Math. Sci. Dept., IBM Res., Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026795227","display_name":"Dmitry Malioutov","orcid":"https://orcid.org/0000-0002-5541-7044"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dmitry Malioutov","raw_affiliation_strings":["Business Analytics and Mathematical Sciences Department, IBM Research Yorktown Heights, NY, USA","Bus. Analytics & Math. Sci. Dept., IBM Res., Yorktown Heights, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Business Analytics and Mathematical Sciences Department, IBM Research Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]},{"raw_affiliation_string":"Bus. Analytics & Math. Sci. Dept., IBM Res., Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]}],"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":0.07917889,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1131","last_page":"1131"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12261","display_name":"Statistical Mechanics and Entropy","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12261","display_name":"Statistical Mechanics and Entropy","score":0.9824000000953674,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.97079998254776,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9631999731063843,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8507931232452393},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7003841400146484},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6762832403182983},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6430115103721619},{"id":"https://openalex.org/keywords/column","display_name":"Column (typography)","score":0.5782325863838196},{"id":"https://openalex.org/keywords/factor","display_name":"Factor (programming language)","score":0.5728634595870972},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5361976623535156},{"id":"https://openalex.org/keywords/factor-analysis","display_name":"Factor analysis","score":0.4936538636684418},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4662797451019287},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4515577256679535},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4201938807964325},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36847758293151855},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3413221836090088}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8507931232452393},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7003841400146484},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6762832403182983},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6430115103721619},{"id":"https://openalex.org/C2780551164","wikidata":"https://www.wikidata.org/wiki/Q2306599","display_name":"Column (typography)","level":3,"score":0.5782325863838196},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.5728634595870972},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5361976623535156},{"id":"https://openalex.org/C10879293","wikidata":"https://www.wikidata.org/wiki/Q726474","display_name":"Factor analysis","level":2,"score":0.4936538636684418},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4662797451019287},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4515577256679535},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4201938807964325},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36847758293151855},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3413221836090088},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","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":"doi:10.1109/globalsip.2013.6737094","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2013.6737094","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Global Conference on Signal and Information Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W2016182223","https://openalex.org/W2109579243"],"related_works":["https://openalex.org/W4256652904","https://openalex.org/W1598178850","https://openalex.org/W2039330702","https://openalex.org/W4213186391","https://openalex.org/W3125847683","https://openalex.org/W1943061646","https://openalex.org/W1985417024","https://openalex.org/W4285697354","https://openalex.org/W1992085319","https://openalex.org/W1575265113"],"abstract_inverted_index":{"Modern":[0],"finance":[1],"has":[2,41],"grown":[3],"increasingly":[4],"high-dimensional,":[5],"with":[6],"tens":[7],"of":[8,10,24,51,73,96,136,143],"thousands":[9],"stocks":[11],"and":[12,14,30,76,111,140],"bonds":[13],"other":[15],"more":[16],"complex":[17],"instruments":[18,53],"that":[19],"are":[20,66,90],"the":[21,48,91,97,105,134,137,141,144],"basic":[22],"units":[23],"strategies":[25],"for":[26],"hedging,":[27],"risk":[28],"management,":[29],"investment.":[31],"The":[32,59,94],"most":[33],"popular":[34],"way":[35],"to":[36,63,115,124],"understand":[37],"this":[38],"intimidating":[39],"complexity":[40],"been":[42],"through":[43],"factor":[44,64,125],"models,":[45],"which":[46,132],"decompose":[47],"whole":[49],"universe":[50],"investment":[52],"into":[54],"a":[55,71],"few":[56],"key":[57,74,92],"drivers.":[58,93],"two":[60],"main":[61],"approaches":[62],"analysis":[65],"fundamental,":[67],"where":[68,78],"analysts":[69],"hand-pick":[70],"set":[72],"drivers,":[75],"statistical,":[77],"algorithmic":[79],"techniques":[80],"such":[81],"as":[82],"Principal":[83],"Component":[84],"Analysis":[85],"(PCA)":[86],"automatically":[87],"determine":[88],"what":[89],"shortcoming":[95],"fundamental":[98,138],"approach":[99,107,123,139],"is":[100,108],"not":[101,109,113],"being":[102],"data-adaptive,":[103],"while":[104],"statistical":[106,145],"interpretable":[110],"does":[112],"lead":[114],"easy":[116],"hedging":[117],"strategies.":[118],"We":[119],"suggest":[120],"an":[121],"alternative":[122],"analysis,":[126],"relying":[127],"on":[128],"column":[129],"subset":[130],"selection,":[131],"keeps":[133],"interpretability":[135],"data-adaptivity":[142],"PCA-based":[146],"approach.":[147]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
