{"id":"https://openalex.org/W2896517425","doi":"https://doi.org/10.1109/tsp.2018.2875419","title":"Recovery of Independent Sparse Sources From Linear Mixtures Using Sparse Bayesian Learning","display_name":"Recovery of Independent Sparse Sources From Linear Mixtures Using Sparse Bayesian Learning","publication_year":2018,"publication_date":"2018-10-16","ids":{"openalex":"https://openalex.org/W2896517425","doi":"https://doi.org/10.1109/tsp.2018.2875419","mag":"2896517425"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2018.2875419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2018.2875419","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://hdl.handle.net/11250/2594115","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005722185","display_name":"Seyyed Hamed Fouladi","orcid":"https://orcid.org/0000-0001-9202-2271"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seyyed Hamed Fouladi","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-9202-2271","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040867232","display_name":"Sung-En Chiu","orcid":"https://orcid.org/0000-0002-4220-2327"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sung-En Chiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001700017","display_name":"Bhaskar D. Rao","orcid":"https://orcid.org/0000-0001-6357-689X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhaskar D. Rao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5020500091","display_name":"Ilangko Balasingham","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ilangko Balasingham","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4727,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.65946262,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"66","issue":"24","first_page":"6332","last_page":"6346"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5719790458679199},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5220865607261658},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.48870208859443665},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.46652230620384216},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.444038987159729},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4214918315410614},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3516484498977661},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.09142929315567017}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5719790458679199},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5220865607261658},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.48870208859443665},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.46652230620384216},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.444038987159729},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4214918315410614},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3516484498977661},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.09142929315567017},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C147597530","wikidata":"https://www.wikidata.org/wiki/Q369472","display_name":"Computational chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tsp.2018.2875419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2018.2875419","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:ntnuopen.ntnu.no:11250/2594115","is_oa":true,"landing_page_url":"http://hdl.handle.net/11250/2594115","pdf_url":null,"source":{"id":"https://openalex.org/S4306401716","display_name":"Duo Research Archive (University of Oslo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184942183","host_organization_name":"University of Oslo","host_organization_lineage":["https://openalex.org/I184942183"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"6332-6346","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:ntnuopen.ntnu.no:11250/2594115","is_oa":true,"landing_page_url":"http://hdl.handle.net/11250/2594115","pdf_url":null,"source":{"id":"https://openalex.org/S4306401716","display_name":"Duo Research Archive (University of Oslo)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184942183","host_organization_name":"University of Oslo","host_organization_lineage":["https://openalex.org/I184942183"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"6332-6346","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":86,"referenced_works":["https://openalex.org/W118387223","https://openalex.org/W340244495","https://openalex.org/W1482957701","https://openalex.org/W1548802052","https://openalex.org/W1578236487","https://openalex.org/W1601421162","https://openalex.org/W1755563775","https://openalex.org/W1774526428","https://openalex.org/W1974774078","https://openalex.org/W1989476724","https://openalex.org/W1995513458","https://openalex.org/W2005741801","https://openalex.org/W2008821584","https://openalex.org/W2010641531","https://openalex.org/W2015838144","https://openalex.org/W2028838544","https://openalex.org/W2029299478","https://openalex.org/W2033419225","https://openalex.org/W2044935899","https://openalex.org/W2050834445","https://openalex.org/W2054173774","https://openalex.org/W2056515034","https://openalex.org/W2059921811","https://openalex.org/W2060546024","https://openalex.org/W2064502866","https://openalex.org/W2065513175","https://openalex.org/W2071284784","https://openalex.org/W2072858761","https://openalex.org/W2097323375","https://openalex.org/W2104164259","https://openalex.org/W2107861471","https://openalex.org/W2110657565","https://openalex.org/W2114119345","https://openalex.org/W2115606304","https://openalex.org/W2116148865","https://openalex.org/W2117987876","https://openalex.org/W2120495964","https://openalex.org/W2120991191","https://openalex.org/W2122315118","https://openalex.org/W2122548617","https://openalex.org/W2126301071","https://openalex.org/W2127870457","https://openalex.org/W2129319777","https://openalex.org/W2133069808","https://openalex.org/W2135046866","https://openalex.org/W2135338342","https://openalex.org/W2136284422","https://openalex.org/W2143132653","https://openalex.org/W2143163931","https://openalex.org/W2145096794","https://openalex.org/W2145841867","https://openalex.org/W2146000945","https://openalex.org/W2147276092","https://openalex.org/W2147549783","https://openalex.org/W2148154358","https://openalex.org/W2151713296","https://openalex.org/W2152279006","https://openalex.org/W2154332973","https://openalex.org/W2160798840","https://openalex.org/W2162409952","https://openalex.org/W2163430887","https://openalex.org/W2164696938","https://openalex.org/W2168287525","https://openalex.org/W2168745297","https://openalex.org/W2169326247","https://openalex.org/W2205924474","https://openalex.org/W2295077720","https://openalex.org/W2296319761","https://openalex.org/W2296616510","https://openalex.org/W2519993341","https://openalex.org/W2537222602","https://openalex.org/W2894923989","https://openalex.org/W3103447662","https://openalex.org/W3141391850","https://openalex.org/W4205778870","https://openalex.org/W4241874243","https://openalex.org/W4250589301","https://openalex.org/W4250955649","https://openalex.org/W4285719527","https://openalex.org/W4292230442","https://openalex.org/W4300263211","https://openalex.org/W6628860809","https://openalex.org/W6675764344","https://openalex.org/W6676940277","https://openalex.org/W6679164043","https://openalex.org/W6684677280"],"related_works":["https://openalex.org/W2990531703","https://openalex.org/W1983610137","https://openalex.org/W1972656095","https://openalex.org/W3047965787","https://openalex.org/W1945544474","https://openalex.org/W2738244484","https://openalex.org/W2115296911","https://openalex.org/W1973406954","https://openalex.org/W2921182884","https://openalex.org/W2607938758"],"abstract_inverted_index":{"Classical":[0],"algorithms":[1,200],"for":[2,13,37,102,118],"the":[3,14,28,34,58,75,116,119,151,157,163,173,182,188,198,207,214],"multiple":[4],"measurement":[5],"vector":[6],"(MMV)":[7],"problem":[8,122],"assume":[9],"either":[10],"independent":[11,48,86,135,144],"columns":[12],"solution":[15,76],"matrix":[16,77,83],"or":[17],"certain":[18,54],"models":[19],"of":[20,60,80,85,156,162,211],"correlation":[21,25],"among\\nthe":[22],"columns.":[23],"The":[24,108],"structure":[26],"in":[27,52,123,213],"previous":[29,124],"MMVformulation":[30],"does":[31],"not":[32],"capture":[33,71],"signals":[35,46,62],"well":[36],"some":[38],"applications":[39],"like":[40],"photoplethysmography":[41],"(PPG)":[42],"signal":[43],"extraction":[44],"where\\nthe":[45],"are":[47,63,184],"and":[49,88,142,159,170,186,204],"linearly":[50],"mixed":[51],"a":[53,81,89,95,100],"manner.":[55],"In":[56,68],"practice,":[57],"mixtures":[59],"these":[61],"observed":[64],"through":[65],"different":[66],"channels.":[67],"order":[69],"to":[70,172],"this":[72,103],"structure,\\nwe":[73],"decompose":[74],"into":[78],"multiplication":[79],"sparse":[82,138,146,185],"composed":[84],"components,":[87],"linear":[90,104,152,215],"mixing":[91,105,216],"matrix.":[92],"We":[93,126],"derive":[94],"new":[96],"condition":[97,109],"that":[98,181,187,197],"guarantees":[99],"unique\\nsolution":[101],"MMV":[106,121,217],"problem.":[107],"can":[110,205],"be":[111],"much":[112],"less":[113],"restrictive":[114],"than":[115],"conditions":[117],"typical":[120,174],"works.":[125],"also":[127,168],"propose":[128],"two":[129],"novel":[130],"sparse\\nBayesian":[131],"learning":[132,140,148],"(SBL)":[133],"algorithms,":[134],"component":[136,145],"analysis":[137],"Bayesian":[139,147],"(ICASBL)":[141],"fast":[143],"(FASTICASBL),":[149],"which\\ncapture":[150],"mixture":[153],"structure.":[154],"Analysis":[155],"global":[158,189],"local":[160],"minima":[161,190],"ICASBL":[164],"cost":[165,176],"function":[166,177],"is":[167,179],"provided,":[169],"similar":[171],"SBL":[175],"it":[178],"shown":[180],"local\\nminima":[183],"have":[191],"maximum":[192],"sparsity.":[193],"Experimental":[194],"results":[195],"show":[196],"proposed":[199],"outperform":[201],"traditional":[202],"approaches":[203],"recover":[206],"signal\\nwith":[208],"fewer":[209],"number":[210],"measurements":[212],"setting.":[218]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-08-08T01:25:22.217667","created_date":"2025-10-10T00:00:00"}
