{"id":"https://openalex.org/W1987352175","doi":"https://doi.org/10.1109/wacv.2014.6836054","title":"Unsupervised dictionary learning with double-layer sparse representation","display_name":"Unsupervised dictionary learning with double-layer sparse representation","publication_year":2014,"publication_date":"2014-03-01","ids":{"openalex":"https://openalex.org/W1987352175","doi":"https://doi.org/10.1109/wacv.2014.6836054","mag":"1987352175"},"language":"en","primary_location":{"id":"doi:10.1109/wacv.2014.6836054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2014.6836054","pdf_url":null,"source":{"id":"https://openalex.org/S4393918690","display_name":"IEEE Winter Conference on Applications of Computer Vision","issn_l":"2472-6737","issn":["2472-6737"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Winter Conference on Applications of Computer Vision","raw_type":"proceedings-article"},"type":"conference-paper","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/A5113544017","display_name":"Mai Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mai Xu","raw_affiliation_strings":["School of Electronic and Information Engineering, Beihang University, Beijing, China","School of Electronic and Information Engineering, Beihang University, No. 37, Xueyuan Road, Beijing, 100191, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"School of Electronic and Information Engineering, Beihang University, No. 37, Xueyuan Road, Beijing, 100191, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103046319","display_name":"Zulin Wang","orcid":"https://orcid.org/0000-0002-1328-7739"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zulin Wang","raw_affiliation_strings":["School of Electronic and Information Engineering, Beihang University, Beijing, China","School of Electronic and Information Engineering, Beihang University, No. 37, Xueyuan Road, Beijing, 100191, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"School of Electronic and Information Engineering, Beihang University, No. 37, Xueyuan Road, Beijing, 100191, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.5391,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.58565788,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"53","issue":null,"first_page":"548","last_page":"555"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9988999962806702,"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":0.9988999962806702,"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/discriminative-model","display_name":"Discriminative model","score":0.8587956428527832},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7364575862884521},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7214565873146057},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7092231512069702},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5977679491043091},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.5897906422615051},{"id":"https://openalex.org/keywords/k-svd","display_name":"K-SVD","score":0.5346490740776062},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.501194953918457},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4874029755592346},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.47000131011009216},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.46562498807907104},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.45126840472221375},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.4330254793167114},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.41884198784828186},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3609038293361664}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8587956428527832},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7364575862884521},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7214565873146057},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7092231512069702},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5977679491043091},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.5897906422615051},{"id":"https://openalex.org/C154771677","wikidata":"https://www.wikidata.org/wiki/Q17098361","display_name":"K-SVD","level":3,"score":0.5346490740776062},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.501194953918457},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4874029755592346},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.47000131011009216},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.46562498807907104},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.45126840472221375},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4330254793167114},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.41884198784828186},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3609038293361664},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv.2014.6836054","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv.2014.6836054","pdf_url":null,"source":{"id":"https://openalex.org/S4393918690","display_name":"IEEE Winter Conference on Applications of Computer Vision","issn_l":"2472-6737","issn":["2472-6737"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Winter Conference on Applications of Computer Vision","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W33214042","https://openalex.org/W1834558362","https://openalex.org/W1972959470","https://openalex.org/W1974480136","https://openalex.org/W1992405901","https://openalex.org/W2020719522","https://openalex.org/W2027805700","https://openalex.org/W2069959554","https://openalex.org/W2082855665","https://openalex.org/W2099321050","https://openalex.org/W2110158442","https://openalex.org/W2121058967","https://openalex.org/W2126337883","https://openalex.org/W2127271355","https://openalex.org/W2135046866","https://openalex.org/W2137937911","https://openalex.org/W2144937830","https://openalex.org/W2145889472","https://openalex.org/W2151693816","https://openalex.org/W2153663612","https://openalex.org/W2157785665","https://openalex.org/W2160547390","https://openalex.org/W2163112044","https://openalex.org/W6601318844","https://openalex.org/W6638405277","https://openalex.org/W6643579956"],"related_works":["https://openalex.org/W2099321050","https://openalex.org/W2890952311","https://openalex.org/W2509955295","https://openalex.org/W2047275718","https://openalex.org/W2034957211","https://openalex.org/W2388952560","https://openalex.org/W110819671","https://openalex.org/W2149282631","https://openalex.org/W2011611369","https://openalex.org/W2204991413"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,23,64,111],"novel":[4],"double-layer":[5],"sparse":[6],"representation":[7],"(DLSR)":[8],"approach":[9,135],"for":[10,26,48,85],"unsupervised":[11,41],"dictionary":[12,17],"learning.":[13],"In":[14],"supervised/unsupervised":[15],"discriminative":[16,24,97],"learning,":[18],"classical":[19],"approaches":[20],"usually":[21],"develop":[22],"term":[25],"learning":[27,49],"multiple":[28],"sub-dictionaries,":[29],"each":[30,52,86],"of":[31,44,51,122,133,140,147],"which":[32],"corresponds":[33],"to":[34,56,70],"one-class":[35],"training":[36,46,87],"image":[37,141],"patches.":[38],"However,":[39],"in":[40,67,102,120,136],"scenario,":[42],"some":[43],"the":[45,72,76,79,83,91,94,99,103,107,123,127,131,137,144],"patches":[47],"sub-dictionaries":[50],"class":[53],"are":[54],"related":[55],"more":[57],"than":[58],"one":[59],"class.":[60],"Thus,":[61],"we":[62],"propose":[63],"DLSR":[65,109],"formulation,":[66,110],"this":[68],"paper,":[69],"impose":[71],"first-layer":[73],"sparsity":[74,81],"on":[75,82],"coefficients":[77],"and":[78,96,143],"second-layer":[80],"classes":[84],"patch,":[88],"embedding":[89],"both":[90],"reconstructive":[92],"(via":[93,98],"first-layer)":[95],"second-layer)":[100],"abilities":[101],"dictionary.":[104],"To":[105],"address":[106],"proposed":[108],"simple":[112],"yet":[113],"effective":[114],"algorithm,":[115],"called":[116],"DLSR-OMP,":[117],"is":[118],"developed":[119],"light":[121],"conventional":[124],"OMP.":[125],"Finally,":[126],"experimental":[128],"results":[129],"show":[130],"effectiveness":[132],"our":[134],"reconstruction":[138],"task":[139,146],"denoising":[142],"clustering":[145],"texture":[148],"segmentation.":[149]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
