{"id":"https://openalex.org/W3009154856","doi":"https://doi.org/10.1109/access.2020.2978237","title":"Element-Wise Adaptive Thresholds for Learned Iterative Shrinkage Thresholding Algorithms","display_name":"Element-Wise Adaptive Thresholds for Learned Iterative Shrinkage Thresholding Algorithms","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3009154856","doi":"https://doi.org/10.1109/access.2020.2978237","mag":"3009154856"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2978237","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2978237","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09023989.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09023989.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100331505","display_name":"Dohyun Kim","orcid":"https://orcid.org/0000-0002-2894-317X"},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Dohyun Kim","raw_affiliation_strings":["Department of Information and Communication Engineering, Inha University, Incheon, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information and Communication Engineering, Inha University, Incheon, South Korea","institution_ids":["https://openalex.org/I191879574"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083539980","display_name":"Daeyoung Park","orcid":"https://orcid.org/0000-0001-8573-3526"},"institutions":[{"id":"https://openalex.org/I191879574","display_name":"Inha University","ror":"https://ror.org/01easw929","country_code":"KR","type":"education","lineage":["https://openalex.org/I191879574"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Daeyoung Park","raw_affiliation_strings":["Department of Information and Communication Engineering, Inha University, Incheon, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-8573-3526","affiliations":[{"raw_affiliation_string":"Department of Information and Communication Engineering, Inha University, Incheon, South Korea","institution_ids":["https://openalex.org/I191879574"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100331505"],"corresponding_institution_ids":["https://openalex.org/I191879574"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":3.2372,"has_fulltext":true,"cited_by_count":24,"citation_normalized_percentile":{"value":0.92357097,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"8","issue":null,"first_page":"45874","last_page":"45886"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9994000196456909,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9944999814033508,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6967511773109436},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.6894406080245972},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.6587432026863098},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6391203999519348},{"id":"https://openalex.org/keywords/element","display_name":"Element (criminal law)","score":0.5865991711616516},{"id":"https://openalex.org/keywords/shrinkage","display_name":"Shrinkage","score":0.5775963068008423},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.5205574631690979},{"id":"https://openalex.org/keywords/threshold-limit-value","display_name":"Threshold limit value","score":0.4861566722393036},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4665412902832031},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4487782418727875},{"id":"https://openalex.org/keywords/iterative-method","display_name":"Iterative method","score":0.4365415871143341},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3658187985420227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.27104365825653076},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24629569053649902},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12796887755393982}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6967511773109436},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.6894406080245972},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.6587432026863098},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6391203999519348},{"id":"https://openalex.org/C200288055","wikidata":"https://www.wikidata.org/wiki/Q2621792","display_name":"Element (criminal law)","level":2,"score":0.5865991711616516},{"id":"https://openalex.org/C180145272","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage","level":2,"score":0.5775963068008423},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.5205574631690979},{"id":"https://openalex.org/C64413873","wikidata":"https://www.wikidata.org/wiki/Q21005","display_name":"Threshold limit value","level":2,"score":0.4861566722393036},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4665412902832031},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4487782418727875},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.4365415871143341},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3658187985420227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.27104365825653076},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24629569053649902},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12796887755393982},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2978237","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2978237","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09023989.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:0357b6901257418dbecb3c0922afbccd","is_oa":true,"landing_page_url":"https://doaj.org/article/0357b6901257418dbecb3c0922afbccd","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 45874-45886 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2978237","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2978237","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09023989.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320323103","display_name":"Agency for Defense Development","ror":"https://ror.org/05fhe0r85"},{"id":"https://openalex.org/F4320334874","display_name":"Defense Acquisition Program Administration","ror":"https://ror.org/04bjg9m96"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3009154856.pdf","grobid_xml":"https://content.openalex.org/works/W3009154856.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1965125844","https://openalex.org/W1980454827","https://openalex.org/W2050968963","https://openalex.org/W2074682976","https://openalex.org/W2082029531","https://openalex.org/W2107861471","https://openalex.org/W2112796928","https://openalex.org/W2115706991","https://openalex.org/W2118103795","https://openalex.org/W2127271355","https://openalex.org/W2129131372","https://openalex.org/W2508393166","https://openalex.org/W2604896999","https://openalex.org/W2619204584","https://openalex.org/W2889134685","https://openalex.org/W2907946893","https://openalex.org/W2963322354","https://openalex.org/W2964232913","https://openalex.org/W2970126910","https://openalex.org/W3098306969","https://openalex.org/W3156744082","https://openalex.org/W3157502956","https://openalex.org/W4206742934","https://openalex.org/W4241652050","https://openalex.org/W6677645113","https://openalex.org/W6754103055","https://openalex.org/W6757612955","https://openalex.org/W6762120954"],"related_works":["https://openalex.org/W2391958761","https://openalex.org/W2765453142","https://openalex.org/W2356780078","https://openalex.org/W2356008845","https://openalex.org/W255134961","https://openalex.org/W2348314720","https://openalex.org/W2243547089","https://openalex.org/W2316482937","https://openalex.org/W4382490379","https://openalex.org/W2846133431"],"abstract_inverted_index":{"In":[0,107],"this":[1],"paper,":[2],"we":[3],"propose":[4],"element-wise":[5,76],"adaptive":[6,52],"threshold":[7,16,53,77,88],"methods":[8],"for":[9,17],"learned":[10],"iterative":[11],"shrinkage":[12],"thresholding":[13],"algorithms.":[14,106],"The":[15],"each":[18],"element":[19,46],"is":[20,28,110,117],"adapted":[21],"in":[22,120],"such":[23],"a":[24,48,55],"way":[25],"that":[26,45,73,93],"it":[27,109],"set":[29],"to":[30,67,112],"be":[31],"smaller":[32],"when":[33],"the":[34,39,60,65,68,74,85,94,99,104,113,121],"previously":[35],"recovered":[36],"estimate":[37],"or":[38],"current":[40],"one-step":[41],"gradient":[42],"descent":[43],"at":[44],"has":[47,80,98],"larger":[49],"value.":[50],"This":[51],"gives":[54],"lower":[56],"misdetection":[57],"probability":[58],"of":[59,123],"true":[61],"support,":[62],"which":[63,116],"speedups":[64],"convergence":[66,82],"optimal":[69],"solution.":[70],"We":[71],"show":[72,92],"proposed":[75,95],"adaption":[78],"method":[79],"better":[81],"rate":[83],"than":[84],"existing":[86],"non-adaptive":[87],"methods.":[89],"Numerical":[90],"results":[91],"neural":[96],"network":[97],"best":[100],"recovery":[101],"performance":[102],"among":[103],"tested":[105],"addition,":[108],"robust":[111],"sparsity":[114],"mismatch,":[115],"very":[118],"desirable":[119],"case":[122],"unknown":[124],"signal":[125],"sparsity.":[126]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2026-05-20T08:49:12.498775","created_date":"2025-10-10T00:00:00"}
