{"id":"https://openalex.org/W3205057309","doi":"https://doi.org/10.1109/access.2021.3120656","title":"Automatic Model Determination for Quaternion NMF","display_name":"Automatic Model Determination for Quaternion NMF","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3205057309","doi":"https://doi.org/10.1109/access.2021.3120656","mag":"3205057309"},"language":"en","primary_location":{"id":"doi:10.1109/access.2021.3120656","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3120656","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2021.3120656","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020911518","display_name":"Giancarlo Sanchez","orcid":"https://orcid.org/0000-0002-8088-1391"},"institutions":[{"id":"https://openalex.org/I19700959","display_name":"Florida International University","ror":"https://ror.org/02gz6gg07","country_code":"US","type":"education","lineage":["https://openalex.org/I19700959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Giancarlo Sanchez","raw_affiliation_strings":["Department of Mathematics and Statistics, Florida International University, Miami, FL, USA"],"raw_orcid":"https://orcid.org/0000-0002-8088-1391","affiliations":[{"raw_affiliation_string":"Department of Mathematics and Statistics, Florida International University, Miami, FL, USA","institution_ids":["https://openalex.org/I19700959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038345001","display_name":"Erik Skau","orcid":"https://orcid.org/0000-0002-7707-0838"},"institutions":[{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Erik Skau","raw_affiliation_strings":["Los Alamos National Laboratory, Los Alamos, NM, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Los Alamos National Laboratory, Los Alamos, NM, USA","institution_ids":["https://openalex.org/I1343871089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079029372","display_name":"Boian S. Alexandrov","orcid":"https://orcid.org/0000-0001-8636-4603"},"institutions":[{"id":"https://openalex.org/I1343871089","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67","country_code":"US","type":"facility","lineage":["https://openalex.org/I1330989302","https://openalex.org/I1343871089","https://openalex.org/I198811213","https://openalex.org/I4210120050"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Boian Alexandrov","raw_affiliation_strings":["Los Alamos National Laboratory, Los Alamos, NM, USA"],"raw_orcid":"https://orcid.org/0000-0001-8636-4603","affiliations":[{"raw_affiliation_string":"Los Alamos National Laboratory, Los Alamos, NM, USA","institution_ids":["https://openalex.org/I1343871089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.4192,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.61302889,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"9","issue":null,"first_page":"152243","last_page":"152249"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10860","display_name":"Speech and Audio Processing","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12946","display_name":"Fractal and DNA sequence analysis","score":0.9728000164031982,"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/quaternion","display_name":"Quaternion","score":0.8040022850036621},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.7219356298446655},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.6781156659126282},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5007896423339844},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.49596795439720154},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.46336978673934937},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4127407371997833},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39627373218536377},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.35705333948135376},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3457040786743164},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.25967419147491455},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.14646896719932556},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.13626453280448914}],"concepts":[{"id":"https://openalex.org/C200127275","wikidata":"https://www.wikidata.org/wiki/Q173853","display_name":"Quaternion","level":2,"score":0.8040022850036621},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.7219356298446655},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.6781156659126282},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5007896423339844},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.49596795439720154},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46336978673934937},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4127407371997833},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39627373218536377},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.35705333948135376},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3457040786743164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25967419147491455},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.14646896719932556},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.13626453280448914},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"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/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/access.2021.3120656","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3120656","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:531a2ddaf1c54bc7ad8b933b2127f1d0","is_oa":true,"landing_page_url":"https://doaj.org/article/531a2ddaf1c54bc7ad8b933b2127f1d0","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 9, Pp 152243-152249 (2021)","raw_type":"article"},{"id":"pmh:oai:osti.gov:1831531","is_oa":true,"landing_page_url":"https://www.osti.gov/biblio/1831531","pdf_url":null,"source":{"id":"https://openalex.org/S4306402487","display_name":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139351228","host_organization_name":"Office of Scientific and Technical Information","host_organization_lineage":["https://openalex.org/I139351228"],"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":null},{"id":"pmh:oai:osti.gov:1831532","is_oa":true,"landing_page_url":"https://www.osti.gov/biblio/1831532","pdf_url":null,"source":{"id":"https://openalex.org/S4306402487","display_name":"OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I139351228","host_organization_name":"Office of Scientific and Technical Information","host_organization_lineage":["https://openalex.org/I139351228"],"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":null}],"best_oa_location":{"id":"doi:10.1109/access.2021.3120656","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2021.3120656","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.44999998807907104,"id":"https://metadata.un.org/sdg/1","display_name":"No poverty"}],"awards":[{"id":"https://openalex.org/G3083956167","display_name":null,"funder_award_id":"20190020DR","funder_id":"https://openalex.org/F4320337547","funder_display_name":"Laboratory Directed Research and Development"},{"id":"https://openalex.org/G6410514744","display_name":null,"funder_award_id":"89233218C-NA000001","funder_id":"https://openalex.org/F4320332369","funder_display_name":"National Nuclear Security Administration"}],"funders":[{"id":"https://openalex.org/F4320332369","display_name":"National Nuclear Security Administration","ror":"https://ror.org/03sk1we31"},{"id":"https://openalex.org/F4320337547","display_name":"Laboratory Directed Research and Development","ror":"https://ror.org/01e41cf67"},{"id":"https://openalex.org/F4320338304","display_name":"Los Alamos National Laboratory","ror":"https://ror.org/01e41cf67"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W634311682","https://openalex.org/W1902027874","https://openalex.org/W1973335304","https://openalex.org/W1987971958","https://openalex.org/W1989286669","https://openalex.org/W1990413623","https://openalex.org/W2059745395","https://openalex.org/W2112828793","https://openalex.org/W2120575676","https://openalex.org/W2133069808","https://openalex.org/W2135637836","https://openalex.org/W2142224505","https://openalex.org/W2142638745","https://openalex.org/W2152061559","https://openalex.org/W2164278908","https://openalex.org/W2816406573","https://openalex.org/W2914427333","https://openalex.org/W2923872554","https://openalex.org/W2962156853","https://openalex.org/W2962918497","https://openalex.org/W3008897992","https://openalex.org/W3014532991","https://openalex.org/W3014715263","https://openalex.org/W3031596102","https://openalex.org/W3036661693","https://openalex.org/W3042082631","https://openalex.org/W3127988379","https://openalex.org/W3193658714","https://openalex.org/W4287666927","https://openalex.org/W6679756777","https://openalex.org/W6790238015","https://openalex.org/W6841020288"],"related_works":["https://openalex.org/W4308269461","https://openalex.org/W4281395053","https://openalex.org/W2020769413","https://openalex.org/W2351387116","https://openalex.org/W2127243424","https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W2361100278","https://openalex.org/W2792706544"],"abstract_inverted_index":{"Nonnegative":[0],"Matrix":[1],"Factorization":[2],"(NMF)":[3],"is":[4,69,171],"a":[5,100,205],"well-known":[6],"method":[7,101],"for":[8,15,102,131],"Blind":[9],"Source":[10],"Separation":[11],"(BSS).":[12],"Recently,":[13],"BSS":[14],"polarized":[16,107],"signals":[17,58,65,93,108,197],"in":[18,33,78,109],"spectropolarimetric":[19,110,207],"data,":[20,111,140],"containing":[21],"both":[22],"polarization":[23],"and":[24,82,141,204],"spectral":[25],"information,":[26,186],"was":[27,31],"introduced.":[28],"This":[29],"information":[30],"encoded":[32],"4-dimensional":[34],"Stokes":[35],"vectors":[36],"represented":[37],"by":[38],"quaternion":[39,57,139,202],"numbers.":[40],"In":[41],"the":[42,47,52,62,104,137,162,165,175,178,193],"proposed":[43],"Quaternion":[44,123],"NMF":[45],"(QNMF),":[46],"common":[48],"challenge":[49],"of":[50,56,64,74,84,91,106,127,136,145,147,153,164,177,196],"determining":[51,103],"(usually)":[53],"unknown":[54],"number":[55,63,76,105,152,195],"remained":[59],"unaddressed.":[60],"Estimating":[61],"(aka":[66],"model":[67],"determination)":[68],"important,":[70],"since":[71],"an":[72],"underestimation":[73],"this":[75],"results":[77],"poor":[79],"source":[80],"separation":[81],"omission":[83],"signals,":[85],"while":[86],"overestimation":[87],"leads":[88],"to":[89,160,199],"extraction":[90],"noisy":[92],"without":[94,183],"physical":[95],"meaning.":[96],"Here,":[97],"we":[98],"introduce":[99],"named":[112],"QNMF<inline-formula>":[113,117,187],"<tex-math":[114,118,156,188],"notation=\"LaTeX\">$k$":[115,119,157,189],"</tex-math></inline-formula>.":[116],"</tex-math></inline-formula>":[120,190],"integrates:":[121],"(a)":[122],"Alternating":[124],"Direction":[125],"Method":[126],"Multipliers":[128],"(QADMM)":[129],"implemented":[130],"QNMF,":[132],"(b)":[133],"random":[134],"resampling":[135],"initial":[138],"(c)":[142],"custom":[143],"clustering":[144],"sets":[146],"QADMM":[148],"solutions":[149],"with":[150],"same":[151],"sources,":[154],"<inline-formula>":[155],"</tex-math></inline-formula>,":[158],"needed":[159],"estimate":[161],"stability":[163,176],"solutions.":[166,179],"The":[167],"appropriate":[168],"latent":[169],"dimension":[170],"determined":[172],"based":[173],"on":[174],"We":[180],"demonstrate":[181],"that,":[182],"any":[184],"prior":[185],"accurately":[191],"extracts":[192],"correct":[194],"used":[198],"generate":[200],"synthetic":[201],"datasets":[203],"benchmark":[206],"data.":[208]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
