{"id":"https://openalex.org/W2789593553","doi":"https://doi.org/10.1109/tnnls.2017.2658953","title":"Homotopy Methods Based on $l_{0}$ -Norm for Compressed Sensing","display_name":"Homotopy Methods Based on $l_{0}$ -Norm for Compressed Sensing","publication_year":2017,"publication_date":"2017-02-15","ids":{"openalex":"https://openalex.org/W2789593553","doi":"https://doi.org/10.1109/tnnls.2017.2658953","mag":"2789593553","pmid":"https://pubmed.ncbi.nlm.nih.gov/28212100"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2017.2658953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2017.2658953","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5048046617","display_name":"Zhengshan Dong","orcid":"https://orcid.org/0000-0002-2179-0027"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengshan Dong","raw_affiliation_strings":["Center for Discrete Mathematics and Theoretical Computer Science, Fuzhou University, Fuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Discrete Mathematics and Theoretical Computer Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100413098","display_name":"Wenxing Zhu","orcid":"https://orcid.org/0000-0002-8698-0024"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxing Zhu","raw_affiliation_strings":["Center for Discrete Mathematics and Theoretical Computer Science, Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-8698-0024","affiliations":[{"raw_affiliation_string":"Center for Discrete Mathematics and Theoretical Computer Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I80947539"],"apc_list":null,"apc_paid":null,"fwci":4.6929,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.95550465,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"29","issue":"4","first_page":"1132","last_page":"1146"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9986000061035156,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9951000213623047,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/homotopy","display_name":"Homotopy","score":0.8562871217727661},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.6492841839790344},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.6286189556121826},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6169060468673706},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5358176827430725},{"id":"https://openalex.org/keywords/homotopy-analysis-method","display_name":"Homotopy analysis method","score":0.5149863362312317},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.48821523785591125},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"Compressed sensing","score":0.46971645951271057},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.4215092658996582},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.366781085729599},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3248785138130188},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16386419534683228},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.06013166904449463}],"concepts":[{"id":"https://openalex.org/C5961521","wikidata":"https://www.wikidata.org/wiki/Q746083","display_name":"Homotopy","level":2,"score":0.8562871217727661},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.6492841839790344},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.6286189556121826},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6169060468673706},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5358176827430725},{"id":"https://openalex.org/C173636693","wikidata":"https://www.wikidata.org/wiki/Q17030668","display_name":"Homotopy analysis method","level":3,"score":0.5149863362312317},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48821523785591125},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.46971645951271057},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.4215092658996582},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.366781085729599},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3248785138130188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16386419534683228},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.06013166904449463},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2017.2658953","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2017.2658953","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:28212100","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/28212100","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4451028710","display_name":"\u57fa\u4e8e\u70ed\u4f20\u5bfc\u65b9\u7a0b\u7684\u8d85\u5927\u89c4\u6a21\u96c6\u6210\u7535\u8def\u5e03\u5c40\u6a21\u578b\u53ca\u5feb\u901f\u7b97\u6cd5\u7814\u7a76","funder_award_id":"61672005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7843065619","display_name":null,"funder_award_id":"11331003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325478","display_name":"Himalayan Institute Hospital Trust","ror":"https://ror.org/01nkphj61"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1964888939","https://openalex.org/W1980454827","https://openalex.org/W1999174140","https://openalex.org/W2004544971","https://openalex.org/W2006262045","https://openalex.org/W2009702064","https://openalex.org/W2021302824","https://openalex.org/W2025079166","https://openalex.org/W2027026120","https://openalex.org/W2028781966","https://openalex.org/W2030161963","https://openalex.org/W2031906930","https://openalex.org/W2056201402","https://openalex.org/W2063978378","https://openalex.org/W2065030431","https://openalex.org/W2100556411","https://openalex.org/W2107861471","https://openalex.org/W2109449402","https://openalex.org/W2112225422","https://openalex.org/W2123023890","https://openalex.org/W2126607811","https://openalex.org/W2140619591","https://openalex.org/W2145096794","https://openalex.org/W2151693816","https://openalex.org/W2161227280","https://openalex.org/W2171534739","https://openalex.org/W2335437633","https://openalex.org/W2528648358","https://openalex.org/W2949483514","https://openalex.org/W2963292690","https://openalex.org/W2963322354","https://openalex.org/W2963579802"],"related_works":["https://openalex.org/W1542224353","https://openalex.org/W1661087619","https://openalex.org/W2116854923","https://openalex.org/W2750730210","https://openalex.org/W2236974868","https://openalex.org/W4312766348","https://openalex.org/W2158224665","https://openalex.org/W2362256999","https://openalex.org/W2007245702","https://openalex.org/W2048873866"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"two":[3,102,115],"homotopy":[4,16,26,52,67],"methods":[5,27,68,103],"for":[6,86],"solving":[7],"the":[8,15,19,29,32,36,39,47,61,65,74,83,90,96,101,105,114,126,130],"compressed":[9],"sensing":[10],"(CS)":[11],"problem,":[12,128],"which":[13],"combine":[14],"technique":[17],"with":[18],"iterative":[20],"hard":[21],"thresholding":[22],"(IHT)":[23],"method.":[24],"The":[25],"overcome":[28],"difficulty":[30],"of":[31,38,46,60,73,89,113,125],"IHT":[33],"method":[34],"on":[35,82],"choice":[37],"regularization":[40],"parameter":[41],"value,":[42],"by":[43,64],"tracing":[44],"solutions":[45,124],"regularized":[48],"problem":[49],"along":[50],"a":[51,70],"path.":[53],"We":[54,76],"prove":[55],"that":[56],"any":[57],"accumulation":[58],"point":[59],"sequences":[62],"generated":[63],"proposed":[66,91],"is":[69,132],"feasible":[71],"solution":[72,88,97],"problem.":[75],"also":[77],"show":[78],"an":[79],"upper":[80],"bound":[81],"sparsity":[84],"level":[85],"each":[87],"methods.":[92],"Moreover,":[93],"to":[94],"improve":[95],"quality,":[98],"we":[99],"modify":[100],"into":[104],"corresponding":[106],"heuristic":[107,116],"algorithms.":[108],"Computational":[109],"experiments":[110],"demonstrate":[111],"effectiveness":[112],"algorithms,":[117],"in":[118],"accurately":[119],"and":[120],"efficiently":[121],"generating":[122],"sparse":[123],"CS":[127],"whether":[129],"observation":[131],"noisy":[133],"or":[134],"not.":[135]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":8},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
