{"id":"https://openalex.org/W3022762647","doi":"https://doi.org/10.1109/bigdata52589.2021.9671676","title":"Compressing large sample data for discriminant analysis","display_name":"Compressing large sample data for discriminant analysis","publication_year":2021,"publication_date":"2021-12-15","ids":{"openalex":"https://openalex.org/W3022762647","doi":"https://doi.org/10.1109/bigdata52589.2021.9671676","mag":"3022762647"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata52589.2021.9671676","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671676","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2005.03858","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071840736","display_name":"Alexander F. Lapanowski","orcid":null},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Alexander F. Lapanowski","raw_affiliation_strings":["Department of Statistics, Texas A&M University, College Station, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Texas A&M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081887582","display_name":"Irina Gaynanova","orcid":"https://orcid.org/0000-0002-4116-0268"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Irina Gaynanova","raw_affiliation_strings":["Department of Statistics, Texas A&M University, College Station, TX, USA","\u2020Texas A&M University"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Texas A&M University, College Station, TX, USA","institution_ids":["https://openalex.org/I91045830"]},{"raw_affiliation_string":"\u2020Texas A&M University","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5071840736"],"corresponding_institution_ids":["https://openalex.org/I91045830"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.00129646,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1068","last_page":"1076"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9997000098228455,"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":0.9997000098228455,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9990000128746033,"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":0.9983000159263611,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.7742358446121216},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6741669178009033},{"id":"https://openalex.org/keywords/quadratic-classifier","display_name":"Quadratic classifier","score":0.564751923084259},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5449260473251343},{"id":"https://openalex.org/keywords/sample-size-determination","display_name":"Sample size determination","score":0.533234179019928},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.5263073444366455},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.5012199878692627},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4996786117553711},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.49601230025291443},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.47491392493247986},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.436244398355484},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4349142909049988},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4347797632217407},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4199668765068054},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.4138086140155792},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.403215229511261},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.29439324140548706},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25910332798957825},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.22726309299468994}],"concepts":[{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.7742358446121216},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6741669178009033},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.564751923084259},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5449260473251343},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.533234179019928},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.5263073444366455},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.5012199878692627},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4996786117553711},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.49601230025291443},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.47491392493247986},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.436244398355484},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4349142909049988},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4347797632217407},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4199668765068054},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4138086140155792},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.403215229511261},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.29439324140548706},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25910332798957825},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.22726309299468994},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/bigdata52589.2021.9671676","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata52589.2021.9671676","pdf_url":null,"source":{"id":"https://openalex.org/S4363607718","display_name":"2021 IEEE International Conference on Big Data (Big Data)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2005.03858","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.03858","pdf_url":"https://arxiv.org/pdf/2005.03858","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:3022762647","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2005.03858.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2005.03858","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2005.03858","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2005.03858","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2005.03858","pdf_url":"https://arxiv.org/pdf/2005.03858","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3022762647.pdf"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1480376833","https://openalex.org/W1480392687","https://openalex.org/W1577871831","https://openalex.org/W1945899805","https://openalex.org/W1971402995","https://openalex.org/W1972065964","https://openalex.org/W1997200791","https://openalex.org/W2007399394","https://openalex.org/W2024050645","https://openalex.org/W2045390367","https://openalex.org/W2047765680","https://openalex.org/W2052044664","https://openalex.org/W2068405010","https://openalex.org/W2083607783","https://openalex.org/W2085927849","https://openalex.org/W2112796928","https://openalex.org/W2118250684","https://openalex.org/W2120565458","https://openalex.org/W2129476886","https://openalex.org/W2168155150","https://openalex.org/W2296250998","https://openalex.org/W2590556189","https://openalex.org/W2619002440","https://openalex.org/W2725138281","https://openalex.org/W2787248994","https://openalex.org/W2963002486","https://openalex.org/W2963459305","https://openalex.org/W2964326941","https://openalex.org/W2965363114","https://openalex.org/W3104038823","https://openalex.org/W6650267568","https://openalex.org/W6682597329","https://openalex.org/W6684803608","https://openalex.org/W6697466023","https://openalex.org/W6734065873","https://openalex.org/W6754310460","https://openalex.org/W6776585219"],"related_works":["https://openalex.org/W3156920115","https://openalex.org/W3021382556","https://openalex.org/W2107854016","https://openalex.org/W2107155781","https://openalex.org/W2231649245","https://openalex.org/W2404421265","https://openalex.org/W3124057966","https://openalex.org/W2886068405","https://openalex.org/W2077922582","https://openalex.org/W1656335132","https://openalex.org/W2116780995","https://openalex.org/W3203109947","https://openalex.org/W3198616475","https://openalex.org/W3128079177","https://openalex.org/W2943408691","https://openalex.org/W3177201527","https://openalex.org/W2122328610","https://openalex.org/W2100531911","https://openalex.org/W3043506432","https://openalex.org/W3036156650"],"abstract_inverted_index":{"Large-sample":[0],"data":[1,5],"became":[2,7],"prevalent":[3],"as":[4],"acquisition":[6],"cheaper":[8],"and":[9,83,130],"easier.":[10],"While":[11],"a":[12,31,70,106],"large":[13,60],"sample":[14,61],"size":[15,62],"has":[16],"theoretical":[17,107],"advantages":[18],"for":[19,74,81],"many":[20],"statistical":[21],"methods,":[22],"it":[23],"presents":[24],"computational":[25,56,124],"challenges.":[26],"Sketching,":[27],"or":[28],"compression,":[29],"is":[30,44],"well-studied":[32],"approach":[33,73,104],"to":[34,59,89,115,136],"address":[35],"these":[36],"issues":[37,57],"in":[38,49,87],"regression":[39],"settings,":[40],"but":[41],"considerably":[42],"less":[43],"known":[45],"about":[46],"its":[47,131],"performance":[48],"classification":[50],"settings.":[51],"Here":[52],"we":[53],"consider":[54],"the":[55,64,76,97,110,116,122,127],"due":[58],"within":[63],"discriminant":[65,85],"analysis":[66],"framework.":[67],"We":[68,101],"propose":[69],"new":[71],"compression":[72,91],"reducing":[75,96],"number":[77,98],"of":[78,99,126],"training":[79],"samples":[80],"linear":[82],"quadratic":[84],"analysis,":[86],"contrast":[88],"existing":[90],"methods":[92],"which":[93],"focus":[94],"on":[95,109],"features.":[100],"support":[102],"our":[103],"with":[105],"bound":[108],"misclassification":[111],"error":[112],"rate":[113],"compared":[114,135],"Bayes":[117],"classifier.":[118],"Empirical":[119],"studies":[120],"confirm":[121],"significant":[123],"gains":[125],"proposed":[128],"method":[129],"superior":[132],"predictive":[133],"ability":[134],"random":[137],"sub-sampling.":[138]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}