{"id":"https://openalex.org/W2797798814","doi":"https://doi.org/10.1109/icassp.2018.8462328","title":"Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies","display_name":"Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies","publication_year":2018,"publication_date":"2018-04-01","ids":{"openalex":"https://openalex.org/W2797798814","doi":"https://doi.org/10.1109/icassp.2018.8462328","mag":"2797798814"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2018.8462328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8462328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1804.03981","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009307883","display_name":"Muhammad Naveed Tabassum","orcid":"https://orcid.org/0000-0001-6893-828X"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Muhammad Naveed Tabassum","raw_affiliation_strings":["Dept. of Signal Processing and Acoustics, Aalto University, Aalto, Finland","Dept. of Signal Processing and Acoustics, Aalto University, P.O. Box 15400, Aalto, FI-00076, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Signal Processing and Acoustics, Aalto University, Aalto, Finland","institution_ids":["https://openalex.org/I9927081"]},{"raw_affiliation_string":"Dept. of Signal Processing and Acoustics, Aalto University, P.O. Box 15400, Aalto, FI-00076, Finland","institution_ids":["https://openalex.org/I9927081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031785324","display_name":"Esa Ollila","orcid":"https://orcid.org/0000-0002-0898-5313"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Esa Ollila","raw_affiliation_strings":["Dept. of Signal Processing and Acoustics, Aalto University, Aalto, Finland","Dept. of Signal Processing and Acoustics, Aalto University, P.O. Box 15400, Aalto, FI-00076, Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Signal Processing and Acoustics, Aalto University, Aalto, Finland","institution_ids":["https://openalex.org/I9927081"]},{"raw_affiliation_string":"Dept. of Signal Processing and Acoustics, Aalto University, P.O. Box 15400, Aalto, FI-00076, Finland","institution_ids":["https://openalex.org/I9927081"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I9927081"],"apc_list":null,"apc_paid":null,"fwci":1.8656,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.83207792,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"83","issue":null,"first_page":"4204","last_page":"4208"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9783999919891357,"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/T12867","display_name":"Advanced Biosensing Techniques and Applications","score":0.9214000105857849,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7775234580039978},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.7580476999282837},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6172526478767395},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5817345380783081},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5663068890571594},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.5330454707145691},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5205212831497192},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5006544589996338},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.41885268688201904},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38821613788604736},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32671603560447693}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7775234580039978},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.7580476999282837},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6172526478767395},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5817345380783081},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5663068890571594},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.5330454707145691},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5205212831497192},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5006544589996338},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.41885268688201904},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38821613788604736},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32671603560447693},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/icassp.2018.8462328","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2018.8462328","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1804.03981","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1804.03981","pdf_url":"https://arxiv.org/pdf/1804.03981","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"raw_type":"text"},{"id":"mag:2797798814","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1804.03981.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.1804.03981","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1804.03981","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1804.03981","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1804.03981","pdf_url":"https://arxiv.org/pdf/1804.03981","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.75,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2797798814.pdf","grobid_xml":"https://content.openalex.org/works/W2797798814.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W1915008591","https://openalex.org/W1966626540","https://openalex.org/W1983005403","https://openalex.org/W2020585594","https://openalex.org/W2044762091","https://openalex.org/W2062125287","https://openalex.org/W2127823872","https://openalex.org/W2137476312","https://openalex.org/W2138218344","https://openalex.org/W2155423555","https://openalex.org/W2171263752","https://openalex.org/W2558276801","https://openalex.org/W2609843459","https://openalex.org/W2729687975"],"related_works":["https://openalex.org/W2964086769","https://openalex.org/W3022499325","https://openalex.org/W2024726488","https://openalex.org/W42998563","https://openalex.org/W3010040433","https://openalex.org/W98619704","https://openalex.org/W2032052034","https://openalex.org/W2030233716","https://openalex.org/W2096472395","https://openalex.org/W3112141573","https://openalex.org/W2270062925","https://openalex.org/W3157635258","https://openalex.org/W1999762767","https://openalex.org/W68332663","https://openalex.org/W2970477786","https://openalex.org/W2154919220","https://openalex.org/W2952701317","https://openalex.org/W3137823125","https://openalex.org/W2548880460","https://openalex.org/W2944577694"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2],"modification":[3],"of":[4,18,66,83,93,103,111],"linear":[5],"discriminant":[6,13],"analysis,":[7],"referred":[8],"to":[9],"as":[10,33,74],"compressive":[11],"regularized":[12],"analysis":[14,17],"(CRDA),":[15],"for":[16,25,61],"high-dimensional":[19],"datasets.":[20],"CRDA":[21,40,112],"is":[22,71,86],"especially":[23],"designed":[24],"feature":[26,62,133],"elimination":[27],"purpose":[28],"and":[29,55,100],"can":[30],"be":[31],"used":[32],"gene":[34],"selection":[35],"method":[36,118],"in":[37,48],"microarray":[38,106],"studies.":[39],"lends":[41],"ideas":[42],"from":[43],"\u2113q,1":[44],"norm":[45],"minimization":[46],"algorithms":[47],"the":[49,67,77,81,90,94,109,116,128],"multiple":[50],"measurement":[51],"vectors":[52],"(MMV)":[53],"model":[54],"utilizes":[56],"joint-sparsity":[57],"promoting":[58],"hard":[59],"thresholding":[60],"elimination.":[63],"A":[64,97],"regularization":[65],"sample":[68,91],"covariance":[69],"matrix":[70],"also":[72],"needed":[73],"we":[75],"consider":[76],"challenging":[78],"scenario":[79],"where":[80],"number":[82],"features":[84],"(variables)":[85],"comparable":[87],"or":[88],"exceeding":[89],"size":[92],"training":[95],"dataset.":[96],"simulation":[98],"study":[99],"four":[101],"examples":[102],"real":[104],"life":[105],"datasets":[107],"evaluate":[108],"performances":[110],"based":[113],"classifiers.":[114],"Overall,":[115],"proposed":[117],"gives":[119],"fewer":[120],"misclassification":[121],"errors":[122],"than":[123],"its":[124],"competitors,":[125],"while":[126],"at":[127],"same":[129],"time":[130],"achieving":[131],"accurate":[132],"selection.":[134]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-18T07:49:30.821534","created_date":"2022-10-01T00:00:00"}