{"id":"https://openalex.org/W2165048513","doi":"https://doi.org/10.1109/icdsp.2011.6004892","title":"Maximum a posteriori estimation approach to sparse recovery","display_name":"Maximum a posteriori estimation approach to sparse recovery","publication_year":2011,"publication_date":"2011-07-01","ids":{"openalex":"https://openalex.org/W2165048513","doi":"https://doi.org/10.1109/icdsp.2011.6004892","mag":"2165048513"},"language":"en","primary_location":{"id":"doi:10.1109/icdsp.2011.6004892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2011.6004892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 17th International Conference on Digital Signal Processing (DSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5004318874","display_name":"Md Mashud Hyder","orcid":"https://orcid.org/0000-0002-5288-7358"},"institutions":[{"id":"https://openalex.org/I78757542","display_name":"University of Newcastle Australia","ror":"https://ror.org/00eae9z71","country_code":"AU","type":"education","lineage":["https://openalex.org/I78757542"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Md Mashud Hyder","raw_affiliation_strings":["School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW, Australia","institution_ids":["https://openalex.org/I78757542"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033583535","display_name":"Kaushik Mahata","orcid":"https://orcid.org/0000-0001-8501-3596"},"institutions":[{"id":"https://openalex.org/I78757542","display_name":"University of Newcastle Australia","ror":"https://ror.org/00eae9z71","country_code":"AU","type":"education","lineage":["https://openalex.org/I78757542"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Kaushik Mahata","raw_affiliation_strings":["School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW, Australia","institution_ids":["https://openalex.org/I78757542"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I78757542"],"apc_list":null,"apc_paid":null,"fwci":1.5905,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.83992849,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":1.0,"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":0.9998999834060669,"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/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.9991999864578247,"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":0.8530001640319824},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7435696721076965},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.6306682825088501},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.611521303653717},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.60337895154953},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.5896305441856384},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5803903341293335},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.564540684223175},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5124843120574951},{"id":"https://openalex.org/keywords/row","display_name":"Row","score":0.49320048093795776},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.4604765474796295},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.43892234563827515},{"id":"https://openalex.org/keywords/row-and-column-spaces","display_name":"Row and column spaces","score":0.4369368851184845},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4212290942668915},{"id":"https://openalex.org/keywords/signal-processing","display_name":"Signal processing","score":0.41015973687171936},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39837124943733215},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.346458375453949},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.3362382650375366},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.33422479033470154},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.3225557804107666},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16837522387504578}],"concepts":[{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.8530001640319824},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7435696721076965},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.6306682825088501},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.611521303653717},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.60337895154953},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.5896305441856384},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5803903341293335},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.564540684223175},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5124843120574951},{"id":"https://openalex.org/C135598885","wikidata":"https://www.wikidata.org/wiki/Q1366302","display_name":"Row","level":2,"score":0.49320048093795776},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.4604765474796295},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.43892234563827515},{"id":"https://openalex.org/C104140500","wikidata":"https://www.wikidata.org/wiki/Q2088159","display_name":"Row and column spaces","level":3,"score":0.4369368851184845},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4212290942668915},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.41015973687171936},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39837124943733215},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.346458375453949},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.3362382650375366},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33422479033470154},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.3225557804107666},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16837522387504578},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icdsp.2011.6004892","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdsp.2011.6004892","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 17th International Conference on Digital Signal Processing (DSP)","raw_type":"proceedings-article"},{"id":"pmh:uon:25255","is_oa":false,"landing_page_url":"http://hdl.handle.net/1959.13/1325372","pdf_url":null,"source":{"id":"https://openalex.org/S4377196471","display_name":"NOVA (University of Newcastle Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I78757542","host_organization_name":"University of Newcastle Australia","host_organization_lineage":["https://openalex.org/I78757542"],"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":"conference paper"}],"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/W1986931325","https://openalex.org/W1999867771","https://openalex.org/W2050834445","https://openalex.org/W2065919982","https://openalex.org/W2078204800","https://openalex.org/W2097323375","https://openalex.org/W2100068253","https://openalex.org/W2119667497","https://openalex.org/W2122315118","https://openalex.org/W2128026227","https://openalex.org/W2128659236","https://openalex.org/W2140499889","https://openalex.org/W2148154358","https://openalex.org/W2151693816","https://openalex.org/W2153909896","https://openalex.org/W2168745297","https://openalex.org/W2296616510","https://openalex.org/W2803670972","https://openalex.org/W4250955649"],"related_works":["https://openalex.org/W2313440505","https://openalex.org/W3123744736","https://openalex.org/W2137691148","https://openalex.org/W1505848319","https://openalex.org/W4281399881","https://openalex.org/W3166006430","https://openalex.org/W4281971614","https://openalex.org/W2049180840","https://openalex.org/W1992086467","https://openalex.org/W3121299875"],"abstract_inverted_index":{"We":[0,34,57,80],"adopt":[1],"a":[2,4,15,32,53,60,86],"maximum":[3],"posteriori":[5],"(MAP)":[6],"estimation":[7],"based":[8],"approach":[9,84],"for":[10],"recovering":[11],"sparse":[12,41,92],"signals":[13],"from":[14,52],"small":[16],"number":[17,87],"of":[18,26,31,39,77,88],"measurements":[19,103],"formed":[20],"by":[21],"computing":[22],"the":[23,27,40,75,78],"inner":[24],"products":[25],"signal":[28,42],"with":[29,101],"rows":[30],"matrix.":[33],"assume":[35],"that":[36,82],"each":[37],"component":[38],"is":[43],"independent":[44],"and":[45,104],"identically":[46],"distributed":[47],"(i.i.d)":[48],"random":[49],"variable":[50],"drawn":[51],"Gaussian":[54],"mixture":[55],"model.":[56],"then":[58],"develop":[59],"suitable":[61],"MAP":[62],"formulation":[63],"which":[64],"results":[65],"in":[66],"an":[67],"iterative":[68],"algorithm.":[69,79],"Simulations":[70],"are":[71],"performed":[72],"to":[73,97],"study":[74],"performance":[76,100],"observe":[81],"our":[83],"has":[85],"advantages":[89],"over":[90],"other":[91],"recovery":[93],"techniques,":[94],"including":[95],"robustness":[96],"noise,":[98],"increased":[99],"limited":[102],"lower":[105],"computation":[106],"time.":[107]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2014,"cited_by_count":2},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}