{"id":"https://openalex.org/W2408634371","doi":"https://doi.org/10.1109/icassp.2016.7472528","title":"A recursive predictive risk estimate for proximal algorithms","display_name":"A recursive predictive risk estimate for proximal algorithms","publication_year":2016,"publication_date":"2016-03-01","ids":{"openalex":"https://openalex.org/W2408634371","doi":"https://doi.org/10.1109/icassp.2016.7472528","mag":"2408634371"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2016.7472528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5021923147","display_name":"Feng Xue","orcid":"https://orcid.org/0000-0003-4671-591X"},"institutions":[{"id":"https://openalex.org/I4210119692","display_name":"Numerical Method (China)","ror":"https://ror.org/02ns8zp42","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210119692"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feng Xue","raw_affiliation_strings":["National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics, Beijing, China","institution_ids":["https://openalex.org/I4210119692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069164037","display_name":"Runle Du","orcid":null},"institutions":[{"id":"https://openalex.org/I4210119692","display_name":"Numerical Method (China)","ror":"https://ror.org/02ns8zp42","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210119692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Runle Du","raw_affiliation_strings":["National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics, Beijing, China","institution_ids":["https://openalex.org/I4210119692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100390361","display_name":"Jiaqi Liu","orcid":"https://orcid.org/0000-0002-2481-6827"},"institutions":[{"id":"https://openalex.org/I4210119692","display_name":"Numerical Method (China)","ror":"https://ror.org/02ns8zp42","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210119692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqi Liu","raw_affiliation_strings":["National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics, Beijing, China"],"affiliations":[{"raw_affiliation_string":"National Key Laboratory of Science and Technology on Test Physics and Numerical Mathematics, Beijing, China","institution_ids":["https://openalex.org/I4210119692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5021923147"],"corresponding_institution_ids":["https://openalex.org/I4210119692"],"apc_list":null,"apc_paid":null,"fwci":0.6079,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.67825322,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"4498","last_page":"4502"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10136","display_name":"Statistical Methods and Inference","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.652777910232544},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5089802742004395}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.652777910232544},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5089802742004395}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2016.7472528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2016.7472528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1578255497","https://openalex.org/W1990381576","https://openalex.org/W2006262045","https://openalex.org/W2017812570","https://openalex.org/W2026114584","https://openalex.org/W2030148064","https://openalex.org/W2031604650","https://openalex.org/W2078186482","https://openalex.org/W2100556411","https://openalex.org/W2113920645","https://openalex.org/W2140742952","https://openalex.org/W2142005762","https://openalex.org/W2164452299","https://openalex.org/W2787894218","https://openalex.org/W2913535645","https://openalex.org/W2963726588","https://openalex.org/W6758920291"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W2051487156","https://openalex.org/W2073681303","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W2382290278","https://openalex.org/W2478288626","https://openalex.org/W4391913857"],"abstract_inverted_index":{"For":[0],"accurate":[1],"signal":[2],"reconstruction,":[3],"proximal":[4,45,63],"gradient":[5],"methods":[6],"generally":[7],"require":[8],"proper":[9],"selection":[10],"of":[11,26,69],"regularization":[12,54],"parameter.":[13,55],"In":[14],"this":[15,107],"paper,":[16],"we":[17,33,60],"develop":[18],"two":[19],"data-driven":[20],"optimization":[21],"schemes,":[22],"based":[23],"on":[24],"minimization":[25],"unbiased":[27],"predictive":[28],"risk":[29],"estimate":[30,39],"(UPRE).":[31],"First,":[32],"propose":[34],"a":[35,66],"recursive":[36,81],"UPRE":[37],"to":[38,51,110],"the":[40,44,53,76,86,90],"prediction":[41],"error":[42],"during":[43],"iterations,":[46],"which":[47],"can":[48],"be":[49],"used":[50],"optimize":[52],"Second,":[56],"for":[57,96],"fast":[58],"optimization,":[59],"parametrize":[61],"each":[62],"iterate":[64],"as":[65],"linear":[67,77],"combination":[68],"few":[70],"elementary":[71],"functions":[72],"(LET),":[73],"and":[74],"solve":[75],"weights":[78],"by":[79],"minimizing":[80],"UPRE.":[82],"We":[83],"further":[84],"exemplify":[85],"proposed":[87],"approaches":[88],"with":[89,114],"basic":[91],"iterative":[92],"shrinkage/thresholding":[93],"(IST)":[94],"algorithms":[95],"\u2113":[97],"<sub":[98],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[99],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[100],"-minimization.":[101],"Numerical":[102],"experiments":[103],"show":[104],"that":[105],"iterating":[106],"process":[108],"leads":[109],"higher":[111],"reconstruction":[112],"accuracy":[113],"remarkably":[115],"faster":[116],"computational":[117],"speed":[118],"than":[119],"standard":[120],"IST.":[121]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}