{"id":"https://openalex.org/W2963869679","doi":"https://doi.org/10.1109/mlsp.2015.7324321","title":"A map approach for &amp;#x2113;&lt;inf&gt;q&lt;/inf&gt;-norm regularized sparse parameter estimation using the EM algorithm","display_name":"A map approach for &amp;#x2113;&lt;inf&gt;q&lt;/inf&gt;-norm regularized sparse parameter estimation using the EM algorithm","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2963869679","doi":"https://doi.org/10.1109/mlsp.2015.7324321","mag":"2963869679"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2015.7324321","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2015.7324321","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5041523340","display_name":"Rodrigo Carvajal","orcid":"https://orcid.org/0000-0002-3336-8683"},"institutions":[{"id":"https://openalex.org/I75778554","display_name":"Federico Santa Mar\u00eda Technical University","ror":"https://ror.org/05510vn56","country_code":"CL","type":"education","lineage":["https://openalex.org/I75778554"]}],"countries":["CL"],"is_corresponding":true,"raw_author_name":"Rodrigo Carvajal","raw_affiliation_strings":["Electronics Engineering Department, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Chile"],"affiliations":[{"raw_affiliation_string":"Electronics Engineering Department, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Chile","institution_ids":["https://openalex.org/I75778554"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040738934","display_name":"Juan C. Ag\u00fcero","orcid":"https://orcid.org/0000-0001-7104-3233"},"institutions":[{"id":"https://openalex.org/I75778554","display_name":"Federico Santa Mar\u00eda Technical University","ror":"https://ror.org/05510vn56","country_code":"CL","type":"education","lineage":["https://openalex.org/I75778554"]}],"countries":["CL"],"is_corresponding":false,"raw_author_name":"Juan C. Aguero","raw_affiliation_strings":["Electronics Engineering Department, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Chile"],"affiliations":[{"raw_affiliation_string":"Electronics Engineering Department, Universidad T\u00e9cnica Federico Santa Mar\u00eda, Chile","institution_ids":["https://openalex.org/I75778554"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035521823","display_name":"Boris I. Godoy","orcid":"https://orcid.org/0000-0003-0304-7907"},"institutions":[{"id":"https://openalex.org/I31746571","display_name":"UNSW Sydney","ror":"https://ror.org/03r8z3t63","country_code":"AU","type":"education","lineage":["https://openalex.org/I31746571"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Boris I. Godoy","raw_affiliation_strings":["School of Electrical Engineering and Telecommunications, The University of New South Wales, Australia"],"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Telecommunications, The University of New South Wales, Australia","institution_ids":["https://openalex.org/I31746571"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033667806","display_name":"Dimitrios Katselis","orcid":"https://orcid.org/0000-0001-7625-3360"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dimitrios Katselis","raw_affiliation_strings":["Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, IL, USA"],"affiliations":[{"raw_affiliation_string":"Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, IL, USA","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5041523340"],"corresponding_institution_ids":["https://openalex.org/I75778554"],"apc_list":null,"apc_paid":null,"fwci":2.5887,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.92083776,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9987000226974487,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9986000061035156,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9984999895095825,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.7886146306991577},{"id":"https://openalex.org/keywords/expectation\u2013maximization-algorithm","display_name":"Expectation\u2013maximization algorithm","score":0.6583566069602966},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5815690755844116},{"id":"https://openalex.org/keywords/norm","display_name":"Norm (philosophy)","score":0.5614256858825684},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5591137409210205},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.5537294149398804},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5489335060119629},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5359732508659363},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.4629541039466858},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.44878458976745605},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.44774091243743896},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3804023861885071},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.24069112539291382},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2209712266921997},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11340716481208801}],"concepts":[{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.7886146306991577},{"id":"https://openalex.org/C182081679","wikidata":"https://www.wikidata.org/wiki/Q1275153","display_name":"Expectation\u2013maximization algorithm","level":3,"score":0.6583566069602966},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5815690755844116},{"id":"https://openalex.org/C191795146","wikidata":"https://www.wikidata.org/wiki/Q3878446","display_name":"Norm (philosophy)","level":2,"score":0.5614256858825684},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5591137409210205},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.5537294149398804},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5489335060119629},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5359732508659363},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.4629541039466858},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44878458976745605},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.44774091243743896},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3804023861885071},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.24069112539291382},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2209712266921997},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11340716481208801},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mlsp.2015.7324321","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2015.7324321","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1506806321","https://openalex.org/W1663973292","https://openalex.org/W1985093013","https://openalex.org/W1998966727","https://openalex.org/W2006277118","https://openalex.org/W2026567992","https://openalex.org/W2038614136","https://openalex.org/W2040296279","https://openalex.org/W2043478128","https://openalex.org/W2049633694","https://openalex.org/W2050834445","https://openalex.org/W2063146103","https://openalex.org/W2064322097","https://openalex.org/W2067633806","https://openalex.org/W2100617191","https://openalex.org/W2117853077","https://openalex.org/W2122759946","https://openalex.org/W2135046866","https://openalex.org/W2144755793","https://openalex.org/W2146571341","https://openalex.org/W2149144946","https://openalex.org/W2168913574","https://openalex.org/W2169103656","https://openalex.org/W2296616510","https://openalex.org/W2298003074","https://openalex.org/W2314640094","https://openalex.org/W2498094064","https://openalex.org/W2802627035","https://openalex.org/W2951036040","https://openalex.org/W3105034597","https://openalex.org/W3105543546","https://openalex.org/W3106108064","https://openalex.org/W4238162340","https://openalex.org/W4246701766","https://openalex.org/W4250955649","https://openalex.org/W4292403327","https://openalex.org/W4302561155","https://openalex.org/W6657111684","https://openalex.org/W6684653394","https://openalex.org/W6684832638","https://openalex.org/W6697430157"],"related_works":["https://openalex.org/W1839961359","https://openalex.org/W2075146114","https://openalex.org/W1783992599","https://openalex.org/W2114899076","https://openalex.org/W2100805585","https://openalex.org/W2045588782","https://openalex.org/W2124697778","https://openalex.org/W2135468550","https://openalex.org/W2133422797","https://openalex.org/W1571591724"],"abstract_inverted_index":{"In":[0],"this":[1,65],"paper,":[2],"Bayesian":[3],"parameter":[4],"estimation":[5,38,90,95],"through":[6,40],"the":[7,10,19,22,33,37,41,51,60,76,103,133,143],"consideration":[8],"of":[9,21,36,43,85,102],"Maximum":[11],"A":[12],"Posteriori":[13],"(MAP)":[14],"criterion":[15],"is":[16,97],"revisited":[17],"under":[18],"prism":[20],"Expectation-Maximization":[23],"(EM)":[24],"algorithm.":[25],"By":[26],"incorporating":[27],"a":[28,128],"sparsity-promoting":[29],"penalty":[30],"term":[31],"in":[32,100],"cost":[34],"function":[35],"problem":[39,96,140],"use":[42],"an":[44],"appropriate":[45],"prior":[46,77],"distribution,":[47,78],"we":[48,67,80,125],"show":[49],"how":[50],"EM":[52,104],"algorithm":[53,131],"can":[54],"be":[55,116],"used":[56],"to":[57,74,88],"efficiently":[58],"solve":[59],"corresponding":[61,93],"optimization":[62],"problem.":[63,91],"To":[64],"end,":[66],"rely":[68],"on":[69],"variance-mean":[70],"Gaussian":[71],"mixtures":[72,87],"(VMGM)":[73],"describe":[75],"while":[79],"incorporate":[81],"many":[82],"nice":[83],"features":[84],"these":[86],"our":[89],"The":[92],"MAP":[94],"completely":[98],"expressed":[99],"terms":[101],"algorithm,":[105],"which":[106],"allows":[107],"for":[108,132],"handling":[109],"nonlinearities":[110],"and":[111,141],"hidden":[112],"variables":[113],"that":[114],"cannot":[115],"easily":[117],"handled":[118],"with":[119],"traditional":[120],"methods.":[121],"For":[122],"comparison":[123],"purposes,":[124],"also":[126],"develop":[127],"Coordinate":[129],"Descent":[130],"\u2113":[134],"<sub":[135],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[136],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">q</sub>":[137],"-norm":[138],"penalized":[139],"present":[142],"performance":[144],"results":[145],"via":[146],"simulations.":[147]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
