{"id":"https://openalex.org/W2889129730","doi":"https://doi.org/10.1109/ssp.2018.8450858","title":"Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization","display_name":"Independent Component Analysis Using Semi-Parametric Density Estimation Via Entropy Maximization","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2889129730","doi":"https://doi.org/10.1109/ssp.2018.8450858","mag":"2889129730"},"language":"en","primary_location":{"id":"doi:10.1109/ssp.2018.8450858","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2018.8450858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5006915006","display_name":"Zois Boukouvalas","orcid":"https://orcid.org/0000-0002-5131-1891"},"institutions":[{"id":"https://openalex.org/I4179309","display_name":"Park University","ror":"https://ror.org/04ngpga37","country_code":"US","type":"education","lineage":["https://openalex.org/I4179309"]},{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zois Boukouvalas","raw_affiliation_strings":["Dept. of ENME College Park, University of Maryland, College Park"],"affiliations":[{"raw_affiliation_string":"Dept. of ENME College Park, University of Maryland, College Park","institution_ids":["https://openalex.org/I66946132","https://openalex.org/I4179309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027564445","display_name":"Yuri Levin\u2010Schwartz","orcid":"https://orcid.org/0000-0003-0494-7990"},"institutions":[{"id":"https://openalex.org/I98704320","display_name":"Icahn School of Medicine at Mount Sinai","ror":"https://ror.org/04a9tmd77","country_code":"US","type":"education","lineage":["https://openalex.org/I1320796813","https://openalex.org/I98704320"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuri Levin-Schwartz","raw_affiliation_strings":["Dept. of EMPH, Icahn School of Medicine at Mount Sinai, New York"],"affiliations":[{"raw_affiliation_string":"Dept. of EMPH, Icahn School of Medicine at Mount Sinai, New York","institution_ids":["https://openalex.org/I98704320"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091833364","display_name":"Rami Mowakeaa","orcid":"https://orcid.org/0000-0002-4256-3392"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rami Mowakeaa","raw_affiliation_strings":["Dept. of CSEE, University of Maryland, Baltimore County, Baltimore"],"affiliations":[{"raw_affiliation_string":"Dept. of CSEE, University of Maryland, Baltimore County, Baltimore","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103070417","display_name":"Geng-Shen Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I1311688040","display_name":"Amazon (United States)","ror":"https://ror.org/04mv4n011","country_code":"US","type":"company","lineage":["https://openalex.org/I1311688040"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Geng-Shen Fu","raw_affiliation_strings":["Amazon.com Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Amazon.com Cambridge, MA, USA","institution_ids":["https://openalex.org/I1311688040"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060798483","display_name":"T\u00fclay Adal\u0131","orcid":"https://orcid.org/0000-0003-0594-2796"},"institutions":[{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tulay Adali","raw_affiliation_strings":["Dept. of CSEE, University of Maryland, Baltimore County, Baltimore"],"affiliations":[{"raw_affiliation_string":"Dept. of CSEE, University of Maryland, Baltimore County, Baltimore","institution_ids":["https://openalex.org/I79272384"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5006915006"],"corresponding_institution_ids":["https://openalex.org/I4179309","https://openalex.org/I66946132"],"apc_list":null,"apc_paid":null,"fwci":0.8257,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.73413777,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"403","last_page":"407"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":1.0,"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":1.0,"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/T10320","display_name":"Neural Networks and Applications","score":0.986299991607666,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9794999957084656,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/independent-component-analysis","display_name":"Independent component analysis","score":0.9411150217056274},{"id":"https://openalex.org/keywords/blind-signal-separation","display_name":"Blind signal separation","score":0.7066162824630737},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6569448113441467},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.6286638975143433},{"id":"https://openalex.org/keywords/entropy-maximization","display_name":"Entropy maximization","score":0.5781011581420898},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.5527662634849548},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.5434529781341553},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.5148957967758179},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4874284565448761},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.47651660442352295},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4677353799343109},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46710434556007385},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.4284496307373047},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.42355769872665405},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.4217890501022339},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38867875933647156},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.31199342012405396},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26703035831451416},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16492506861686707},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.145894855260849}],"concepts":[{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.9411150217056274},{"id":"https://openalex.org/C120317606","wikidata":"https://www.wikidata.org/wiki/Q17105967","display_name":"Blind signal separation","level":3,"score":0.7066162824630737},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6569448113441467},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.6286638975143433},{"id":"https://openalex.org/C127233936","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Entropy maximization","level":3,"score":0.5781011581420898},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.5527662634849548},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.5434529781341553},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.5148957967758179},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4874284565448761},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.47651660442352295},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4677353799343109},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46710434556007385},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.4284496307373047},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.42355769872665405},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.4217890501022339},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38867875933647156},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.31199342012405396},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26703035831451416},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16492506861686707},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.145894855260849},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","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},{"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/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"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/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssp.2018.8450858","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssp.2018.8450858","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1597348525","https://openalex.org/W1755563775","https://openalex.org/W1782590233","https://openalex.org/W1991840148","https://openalex.org/W2009070933","https://openalex.org/W2056423366","https://openalex.org/W2098017479","https://openalex.org/W2099111195","https://openalex.org/W2108384452","https://openalex.org/W2115074110","https://openalex.org/W2136284422","https://openalex.org/W2141224535","https://openalex.org/W2141637993","https://openalex.org/W2210387432","https://openalex.org/W2784918977","https://openalex.org/W4285719527","https://openalex.org/W4298091452","https://openalex.org/W4301014524","https://openalex.org/W6748222190"],"related_works":["https://openalex.org/W2390344110","https://openalex.org/W2364896863","https://openalex.org/W2361066326","https://openalex.org/W2046761971","https://openalex.org/W2182042810","https://openalex.org/W2130656060","https://openalex.org/W2156932837","https://openalex.org/W2380698615","https://openalex.org/W374502268","https://openalex.org/W2103029460"],"abstract_inverted_index":{"Independent":[0],"component":[1],"analysis":[2],"(ICA)":[3],"is":[4,36,53],"one":[5],"of":[6,19,34,41,47,89,97],"the":[7,42,48,87,90],"most":[8],"popular":[9],"methods":[10],"for":[11],"blind":[12],"source":[13],"separation":[14],"with":[15,71],"a":[16,61,94],"diverse":[17],"set":[18],"applications,":[20],"such":[21],"as:":[22],"biomedical":[23],"signal":[24],"processing,":[25],"video":[26],"and":[27,30,63,78,100],"image":[28],"analysis,":[29],"communications.":[31],"The":[32],"success":[33],"ICA":[35,65,107],"tied":[37],"to":[38,84],"proper":[39],"characterization":[40],"probability":[43],"density":[44],"function":[45],"(PDF)":[46],"latent":[49],"sources;":[50],"information":[51],"that":[52],"generally":[54],"unknown.":[55],"In":[56],"this":[57],"work,":[58],"we":[59],"propose":[60],"new":[62],"efficient":[64],"algorithm":[66],"based":[67],"on":[68],"entropy":[69],"maximization":[70],"kernels,":[72],"(ICA-EMK),":[73],"which":[74],"uses":[75],"both":[76],"global":[77],"local":[79],"measuring":[80],"functions":[81],"as":[82,111,113],"constraints":[83],"dynamically":[85],"estimate":[86],"PDF":[88],"sources.":[91],"We":[92],"present":[93],"mathematical":[95],"justification":[96],"its":[98,102],"convergence":[99],"demonstrate":[101],"superior":[103],"performance":[104],"over":[105],"competing":[106],"algorithms":[108],"using":[109],"simulated":[110],"well":[112],"real-world":[114],"data.":[115]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":2}],"updated_date":"2026-01-13T01:12:25.745995","created_date":"2025-10-10T00:00:00"}