{"id":"https://openalex.org/W3198226765","doi":"https://doi.org/10.1109/lsp.2021.3135196","title":"Efficient ADMM-Based Algorithms for Convolutional Sparse Coding","display_name":"Efficient ADMM-Based Algorithms for Convolutional Sparse Coding","publication_year":2021,"publication_date":"2021-12-14","ids":{"openalex":"https://openalex.org/W3198226765","doi":"https://doi.org/10.1109/lsp.2021.3135196","mag":"3198226765"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2021.3135196","is_oa":true,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3135196","pdf_url":"https://ieeexplore.ieee.org/ielx7/97/9686799/09650706.pdf","source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://ieeexplore.ieee.org/ielx7/97/9686799/09650706.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071731476","display_name":"Farshad G. Veshki","orcid":"https://orcid.org/0000-0002-9832-6783"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Farshad Veshki","raw_affiliation_strings":["Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland"],"raw_orcid":"https://orcid.org/0000-0002-9832-6783","affiliations":[{"raw_affiliation_string":"Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland","institution_ids":["https://openalex.org/I9927081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055234860","display_name":"Sergiy A. Vorobyov","orcid":"https://orcid.org/0000-0001-7249-647X"},"institutions":[{"id":"https://openalex.org/I9927081","display_name":"Aalto University","ror":"https://ror.org/020hwjq30","country_code":"FI","type":"education","lineage":["https://openalex.org/I9927081"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Sergiy Vorobyov","raw_affiliation_strings":["Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland"],"raw_orcid":"https://orcid.org/0000-0001-7249-647X","affiliations":[{"raw_affiliation_string":"Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland","institution_ids":["https://openalex.org/I9927081"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I9927081"],"apc_list":null,"apc_paid":null,"fwci":2.1282,"has_fulltext":true,"cited_by_count":18,"citation_normalized_percentile":{"value":0.86273815,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"29","issue":null,"first_page":"389","last_page":"393"},"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/T11233","display_name":"Advanced Adaptive Filtering Techniques","score":0.9983999729156494,"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":0.9962000250816345,"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/convolutional-code","display_name":"Convolutional code","score":0.8421226739883423},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.655684232711792},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6445168256759644},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5980898141860962},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5326836705207825},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.47262951731681824},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.4158388674259186},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36940860748291016},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3381384015083313},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27338945865631104},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.1668720543384552}],"concepts":[{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.8421226739883423},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.655684232711792},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6445168256759644},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5980898141860962},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5326836705207825},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.47262951731681824},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.4158388674259186},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36940860748291016},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3381384015083313},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27338945865631104},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.1668720543384552},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/lsp.2021.3135196","is_oa":true,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3135196","pdf_url":"https://ieeexplore.ieee.org/ielx7/97/9686799/09650706.pdf","source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2109.02969","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.02969","pdf_url":"https://arxiv.org/pdf/2109.02969","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"pmh:oai:aaltodoc.aalto.fi:123456789/113171","is_oa":true,"landing_page_url":"https://research.aalto.fi/en/publications/1d5af45e-2e45-4676-b874-668687617696","pdf_url":"https://research.aalto.fi/files/80049859/Efficient_ADMM_Based_Algorithms_for_Convolutional_Sparse_Coding.pdf","source":{"id":"https://openalex.org/S4306401662","display_name":"Aaltodoc (Aalto University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I9927081","host_organization_name":"Aalto University","host_organization_lineage":["https://openalex.org/I9927081"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"A1 Alkuper\u00e4isartikkeli tieteellisess\u00e4 aikakauslehdess\u00e4"}],"best_oa_location":{"id":"doi:10.1109/lsp.2021.3135196","is_oa":true,"landing_page_url":"https://doi.org/10.1109/lsp.2021.3135196","pdf_url":"https://ieeexplore.ieee.org/ielx7/97/9686799/09650706.pdf","source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.4399999976158142}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3198226765.pdf","grobid_xml":"https://content.openalex.org/works/W3198226765.grobid-xml"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1946953458","https://openalex.org/W2061225176","https://openalex.org/W2115429828","https://openalex.org/W2117259536","https://openalex.org/W2121058967","https://openalex.org/W2127271355","https://openalex.org/W2129812935","https://openalex.org/W2153663612","https://openalex.org/W2160547390","https://openalex.org/W2163886442","https://openalex.org/W2164278908","https://openalex.org/W2190662802","https://openalex.org/W2202656999","https://openalex.org/W2464641472","https://openalex.org/W2488501602","https://openalex.org/W2532801510","https://openalex.org/W2587310949","https://openalex.org/W2613716286","https://openalex.org/W2700340246","https://openalex.org/W2773809991","https://openalex.org/W2790083234","https://openalex.org/W2798401637","https://openalex.org/W2912696054","https://openalex.org/W2924878362","https://openalex.org/W2952071070","https://openalex.org/W2963368219","https://openalex.org/W2998320011","https://openalex.org/W3000380704","https://openalex.org/W3015267831","https://openalex.org/W3105465046","https://openalex.org/W3105689206","https://openalex.org/W3118294144","https://openalex.org/W3118482223","https://openalex.org/W3126418508","https://openalex.org/W3126849822","https://openalex.org/W3127013675","https://openalex.org/W3182357860","https://openalex.org/W4292363360"],"related_works":["https://openalex.org/W2890544631","https://openalex.org/W2067062989","https://openalex.org/W4205656132","https://openalex.org/W3004790527","https://openalex.org/W2203155458","https://openalex.org/W2111634407","https://openalex.org/W2783282829","https://openalex.org/W2138494306","https://openalex.org/W2539392819","https://openalex.org/W212310357"],"abstract_inverted_index":{"Convolutional":[0],"sparse":[1,7,19,93],"coding":[2,20,94],"improves":[3,63],"on":[4,24,98],"the":[5,25,32,64,68,99,105],"standard":[6],"approximation":[8,100],"by":[9],"incorporating":[10],"a":[11,46,56,88,96],"global":[12],"shift-invariant":[13],"model.":[14],"The":[15,35,71],"most":[16],"efficient":[17,80],"convolutional":[18,47,92],"methods":[21,41],"are":[22,108],"based":[23],"alternating":[26],"direction":[27],"method":[28],"of":[29,67],"multipliers":[30],"and":[31],"convolution":[33],"theorem.":[34],"only":[36],"major":[37],"difference":[38],"between":[39],"these":[40],"is":[42,74],"how":[43],"they":[44],"approach":[45,73],"least-squares":[48],"fitting":[49],"subproblem.":[50],"In":[51,84],"this":[52,60],"letter,":[53],"we":[54,86],"present":[55],"novel":[57,89],"solution":[58],"for":[59,91,104],"subproblem,":[61],"which":[62],"computational":[65],"efficiency":[66],"existing":[69],"algorithms.":[70],"same":[72],"also":[75],"used":[76],"to":[77],"develop":[78],"an":[79],"dictionary":[81],"learning":[82],"method.":[83],"addition,":[85],"propose":[87],"algorithm":[90],"with":[95],"constraint":[97],"error.":[101],"Source":[102],"codes":[103],"proposed":[106],"algorithms":[107],"available":[109],"online.":[110]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}
