{"id":"https://openalex.org/W4302011305","doi":"https://doi.org/10.48550/arxiv.2210.00418","title":"Subspace Learning for Feature Selection via Rank Revealing QR Factorization: Unsupervised and Hybrid Approaches with Non-negative Matrix Factorization and Evolutionary Algorithm","display_name":"Subspace Learning for Feature Selection via Rank Revealing QR Factorization: Unsupervised and Hybrid Approaches with Non-negative Matrix Factorization and Evolutionary Algorithm","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4302011305","doi":"https://doi.org/10.48550/arxiv.2210.00418"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.00418","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_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2210.00418","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103056474","display_name":"Amir Moslemi","orcid":"https://orcid.org/0000-0003-0849-1943"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moslemi, Amir","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5003556123","display_name":"Arash Ahmadian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmadian, Arash","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.618255,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":60,"max":70},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10885","display_name":"Gene expression and cancer classification","score":0.9839,"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":0.9839,"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/T10057","display_name":"Face and Expression Recognition","score":0.9661,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9247,"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/non-negative-matrix-factorization","display_name":"Non-negative Matrix Factorization","score":0.65551823},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.50139403},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4595837},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.44493946},{"id":"https://openalex.org/keywords/qr-decomposition","display_name":"QR decomposition","score":0.43814394}],"concepts":[{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.7024188},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.69471824},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.65551823},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.633397},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.57341015},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.5320476},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.51067716},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.50139403},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4595837},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.44493946},{"id":"https://openalex.org/C188060507","wikidata":"https://www.wikidata.org/wiki/Q653242","display_name":"QR decomposition","level":3,"score":0.43814394},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41191575},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.00418","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_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.00418","pdf_url":"http://arxiv.org/pdf/2210.00418","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2210.00418","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_indexed_in_scopus":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.00418","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_indexed_in_scopus":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":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"score":0.73,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4390394189","https://openalex.org/W2951579230","https://openalex.org/W2792706544","https://openalex.org/W2613627778","https://openalex.org/W2539013788","https://openalex.org/W2150953077","https://openalex.org/W2127243424","https://openalex.org/W2037504162","https://openalex.org/W2002598339","https://openalex.org/W1568451138"],"abstract_inverted_index":{"The":[0,45,186],"selection":[1,35,50,99,111,138,144,200],"of":[2,48,107,178,225],"most":[3,91,175],"informative":[4,92],"and":[5,21,155,173,193,205,220],"discriminative":[6,176],"features":[7,93,167,179],"from":[8],"high-dimensional":[9],"data":[10,22],"has":[11,36],"been":[12],"noticed":[13],"as":[14,29,38,94,133,151,159],"an":[15],"important":[16],"topic":[17,41],"in":[18,42,65,88,202],"machine":[19],"learning":[20],"engineering.":[23],"Using":[24],"matrix":[25,31,52,106,128],"factorization-based":[26],"techniques":[27],"such":[28],"nonnegative":[30],"factorization":[32,53,119,123,129,172],"for":[33,109],"feature":[34,43,49,98,110,137,143,199,241],"emerged":[37],"a":[39,57,66,95,114,134,141,152,156,160],"hot":[40],"selection.":[44,242],"main":[46],"goal":[47],"using":[51,170,182,217],"is":[54,78,86,113,124,146,236],"to":[55,117,190],"extract":[56],"subspace":[58],"which":[59,77,112],"approximates":[60],"the":[61,90,104,174,183,228,233,239],"original":[62],"space":[63],"but":[64],"lower":[67],"dimension.":[68],"In":[69,163,223],"this":[70,118,164],"study,":[71],"rank":[72],"revealing":[73],"QR":[74,108,122],"(RRQR)":[75],"factorization,":[76],"computationally":[79],"cheaper":[80],"than":[81],"singular":[82],"value":[83],"decomposition":[84],"(SVD),":[85],"leveraged":[87],"obtaining":[89],"novel":[96],"unsupervised":[97,136],"technique.":[100,162],"This":[101],"technique":[102],"uses":[103],"permutation":[105],"unique":[115],"property":[116],"method.":[120,139],"Moreover,":[121],"embedded":[125],"into":[126],"non-negative":[127],"(NMF)":[130],"objective":[131],"function":[132],"new":[135],"Lastly,":[140],"hybrid":[142],"algorithm":[145,158,188],"proposed":[147,187,234],"by":[148],"coupling":[149],"RRQR,":[150],"filter-based":[153],"technique,":[154],"Genetic":[157,184],"wrapper-based":[161],"method,":[165],"redundant":[166],"are":[168,180,210],"removed":[169],"RRQR":[171],"subset":[177],"selected":[181],"algorithm.":[185],"shows":[189,231],"be":[191],"dependable":[192],"robust":[194],"when":[195],"compared":[196],"against":[197],"state-of-the-art":[198,240],"algorithms":[201],"supervised,":[203],"unsupervised,":[204],"semi-supervised":[206],"settings.":[207],"All":[208],"methods":[209],"tested":[211],"on":[212],"seven":[213],"available":[214],"microarray":[215],"datasets":[216],"KNN,":[218],"SVM":[219],"C4.5":[221],"classifiers.":[222],"terms":[224],"evaluation":[226],"metrics,":[227],"experimental":[229],"results":[230],"that":[232],"method":[235],"comparable":[237],"with":[238]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4302011305","counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-01-20T18:22:28.068611","created_date":"2022-10-06"}