{"id":"https://openalex.org/W3197047746","doi":"https://doi.org/10.1142/s0218001421510095","title":"Efficient Convolutional Dictionary Learning Using Preconditioned ADMM","display_name":"Efficient Convolutional Dictionary Learning Using Preconditioned ADMM","publication_year":2021,"publication_date":"2021-07-01","ids":{"openalex":"https://openalex.org/W3197047746","doi":"https://doi.org/10.1142/s0218001421510095","mag":"3197047746"},"language":"en","primary_location":{"id":"doi:10.1142/s0218001421510095","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001421510095","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-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/A5100429428","display_name":"Xuesong Zhang","orcid":"https://orcid.org/0000-0003-3185-5100"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xuesong Zhang","raw_affiliation_strings":["School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, P.\u00a0R.\u00a0China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, P.\u00a0R.\u00a0China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029990373","display_name":"Baoping Li","orcid":"https://orcid.org/0000-0001-7951-1740"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoping Li","raw_affiliation_strings":["School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, P.\u00a0R.\u00a0China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, P.\u00a0R.\u00a0China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057139422","display_name":"Jing Jiang","orcid":"https://orcid.org/0000-0001-5301-7779"},"institutions":[{"id":"https://openalex.org/I114234892","display_name":"Beijing Union University","ror":"https://ror.org/01hg31662","country_code":"CN","type":"education","lineage":["https://openalex.org/I114234892"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Jiang","raw_affiliation_strings":["Department of Communication Engineering, Beijing Union University, 100101, Beijing, P.\u00a0R.\u00a0China"],"affiliations":[{"raw_affiliation_string":"Department of Communication Engineering, Beijing Union University, 100101, Beijing, P.\u00a0R.\u00a0China","institution_ids":["https://openalex.org/I114234892"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100429428"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12957882,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"35","issue":"09","first_page":"2151009","last_page":"2151009"},"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/T11739","display_name":"Microwave Imaging and Scattering Analysis","score":0.9986000061035156,"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"}},{"id":"https://openalex.org/T11609","display_name":"Geophysical Methods and Applications","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/computer-science","display_name":"Computer science","score":0.7341510057449341},{"id":"https://openalex.org/keywords/k-svd","display_name":"K-SVD","score":0.5309414863586426},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.48920273780822754},{"id":"https://openalex.org/keywords/inversion","display_name":"Inversion (geology)","score":0.4469529390335083},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4392751455307007},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4201539158821106},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.41355255246162415},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.3113142251968384},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.08809468150138855}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7341510057449341},{"id":"https://openalex.org/C154771677","wikidata":"https://www.wikidata.org/wiki/Q17098361","display_name":"K-SVD","level":3,"score":0.5309414863586426},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48920273780822754},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.4469529390335083},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4392751455307007},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4201539158821106},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.41355255246162415},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.3113142251968384},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.08809468150138855},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C109007969","wikidata":"https://www.wikidata.org/wiki/Q749565","display_name":"Structural basin","level":2,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1142/s0218001421510095","is_oa":false,"landing_page_url":"https://doi.org/10.1142/s0218001421510095","pdf_url":null,"source":{"id":"https://openalex.org/S41486457","display_name":"International Journal of Pattern Recognition and Artificial Intelligence","issn_l":"0218-0014","issn":["0218-0014","1793-6381"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319815","host_organization_name":"World Scientific","host_organization_lineage":["https://openalex.org/P4310319815"],"host_organization_lineage_names":["World Scientific"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Pattern Recognition and Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G4218989130","display_name":null,"funder_award_id":"61871055","funder_id":"https://openalex.org/F4320335577","funder_display_name":"Major Research Plan"}],"funders":[{"id":"https://openalex.org/F4320335577","display_name":"Major Research Plan","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1946953458","https://openalex.org/W1967138577","https://openalex.org/W2016910236","https://openalex.org/W2053514113","https://openalex.org/W2077970850","https://openalex.org/W2092663520","https://openalex.org/W2100556411","https://openalex.org/W2117248802","https://openalex.org/W2117259536","https://openalex.org/W2117865218","https://openalex.org/W2130835014","https://openalex.org/W2148791483","https://openalex.org/W2151646056","https://openalex.org/W2160547390","https://openalex.org/W2170608472","https://openalex.org/W2177347332","https://openalex.org/W2190662802","https://openalex.org/W2202656999","https://openalex.org/W2293078015","https://openalex.org/W2304846792","https://openalex.org/W2327302159","https://openalex.org/W2362164969","https://openalex.org/W2570594503","https://openalex.org/W2607685496","https://openalex.org/W2700340246","https://openalex.org/W2732096896","https://openalex.org/W2773809991","https://openalex.org/W2792944346","https://openalex.org/W2912696054","https://openalex.org/W2924878362","https://openalex.org/W2950798207","https://openalex.org/W2952071070","https://openalex.org/W2998320011","https://openalex.org/W3042925017","https://openalex.org/W3103490597","https://openalex.org/W3105689206","https://openalex.org/W3125355101","https://openalex.org/W4229650096","https://openalex.org/W4232921201","https://openalex.org/W4292363360","https://openalex.org/W4312258136"],"related_works":["https://openalex.org/W2890952311","https://openalex.org/W2509955295","https://openalex.org/W2047275718","https://openalex.org/W2034957211","https://openalex.org/W2388952560","https://openalex.org/W2080187108","https://openalex.org/W2149282631","https://openalex.org/W2016265625","https://openalex.org/W2011611369","https://openalex.org/W2552089492"],"abstract_inverted_index":{"Given":[0],"training":[1,25],"data,":[2],"convolutional":[3,19],"dictionary":[4],"learning":[5,60],"(CDL)":[6],"seeks":[7],"a":[8,16,23,43],"translation-invariant":[9],"sparse":[10],"representation,":[11],"which":[12,102],"is":[13,128],"characterized":[14],"by":[15,66],"set":[17,26],"of":[18,71,80,120],"kernels.":[20],"However,":[21],"even":[22],"small":[24],"with":[27,95],"moderate":[28],"sample":[29],"size":[30],"can":[31,63],"render":[32],"the":[33,54,58,67,78,105,111,117,121,125],"optimization":[34,45],"process":[35],"both":[36],"computationally":[37],"challenging":[38],"and":[39,86],"memory":[40,112],"starving.":[41],"Under":[42],"biconvex":[44],"strategy":[46],"for":[47],"CDL,":[48],"we":[49],"propose":[50],"to":[51,77],"diagonally":[52],"precondition":[53],"system":[55],"matrices":[56],"in":[57,93],"filter":[59],"sub-problem":[61],"that":[62],"be":[64],"solved":[65],"alternating":[68],"direction":[69],"method":[70,75,123],"multipliers":[72],"(ADMM).":[73],"This":[74],"leads":[76],"substitution":[79],"matrix":[81,87],"inversion":[82],"([Formula:":[83,89,99],"see":[84,90,100],"text]":[85,91],"multiplication":[88],"involved":[92],"ADMM":[94],"an":[96],"element-wise":[97],"operation":[98],"text],":[101],"significantly":[103],"reduces":[104],"computational":[106],"complexity":[107],"as":[108,110],"well":[109],"requirement.":[113],"Numerical":[114],"experiments":[115],"validate":[116],"performance":[118],"advantage":[119],"proposed":[122],"over":[124],"state-of-the-arts.":[126],"Code":[127],"available":[129],"at":[130],"https://github.com/baopingli/Efficient-Convolutional-Dictionary-Learning-using-PADMM":[131],".":[132]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
