{"id":"https://openalex.org/W2543892465","doi":"https://doi.org/10.1109/tip.2017.2729885","title":"Fast Low-Rank Shared Dictionary Learning for Image Classification","display_name":"Fast Low-Rank Shared Dictionary Learning for Image Classification","publication_year":2017,"publication_date":"2017-07-20","ids":{"openalex":"https://openalex.org/W2543892465","doi":"https://doi.org/10.1109/tip.2017.2729885","mag":"2543892465","pmid":"https://pubmed.ncbi.nlm.nih.gov/28742035"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2017.2729885","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2017.2729885","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1610.08606","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Tiep Huu Vu","orcid":"https://orcid.org/0000-0002-3134-9008"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tiep Huu Vu","raw_affiliation_strings":["School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA, USA"],"raw_orcid":"https://orcid.org/0000-0002-3134-9008","affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"last","author":{"id":null,"display_name":"Vishal Monga","orcid":null},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vishal Monga","raw_affiliation_strings":["School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I130769515"],"apc_list":null,"apc_paid":null,"fwci":7.6508,"has_fulltext":false,"cited_by_count":172,"citation_normalized_percentile":{"value":0.98516051,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"26","issue":"11","first_page":"5160","last_page":"5175"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.3003000020980835,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.3003000020980835,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.26809999346733093,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.2451999932527542,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/dictionary-learning","display_name":"Dictionary learning","score":0.7329999804496765},{"id":"https://openalex.org/keywords/k-svd","display_name":"K-SVD","score":0.6697999835014343},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6341000199317932},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5644000172615051},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5270000100135803},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5026999711990356},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.48969998955726624},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4410000145435333}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.760200023651123},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.7329999804496765},{"id":"https://openalex.org/C154771677","wikidata":"https://www.wikidata.org/wiki/Q17098361","display_name":"K-SVD","level":3,"score":0.6697999835014343},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6535999774932861},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6341000199317932},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5644000172615051},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5270000100135803},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5026999711990356},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.48969998955726624},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4410000145435333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38850000500679016},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.295199990272522},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C24138899","wikidata":"https://www.wikidata.org/wiki/Q17141258","display_name":"Instance-based learning","level":3,"score":0.27480000257492065},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2574999928474426}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tip.2017.2729885","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2017.2729885","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},{"id":"pmid:28742035","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/28742035","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 image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:arXiv.org:1610.08606","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1610.08606","pdf_url":"https://arxiv.org/pdf/1610.08606","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":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1610.08606","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1610.08606","pdf_url":"https://arxiv.org/pdf/1610.08606","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":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G310154815","display_name":null,"funder_award_id":"0401531 UP719Z0","funder_id":"https://openalex.org/F4320337345","funder_display_name":"Office of Naval Research"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W25662080","https://openalex.org/W96319384","https://openalex.org/W129703402","https://openalex.org/W1506167217","https://openalex.org/W1835905048","https://openalex.org/W1963932623","https://openalex.org/W1966443746","https://openalex.org/W1977674021","https://openalex.org/W1979158807","https://openalex.org/W1982405594","https://openalex.org/W1988898685","https://openalex.org/W1992405901","https://openalex.org/W1997565609","https://openalex.org/W2004465977","https://openalex.org/W2007588387","https://openalex.org/W2019278291","https://openalex.org/W2027805700","https://openalex.org/W2030026059","https://openalex.org/W2032768707","https://openalex.org/W2037696125","https://openalex.org/W2043509464","https://openalex.org/W2043702520","https://openalex.org/W2065337811","https://openalex.org/W2084716923","https://openalex.org/W2094670612","https://openalex.org/W2100556411","https://openalex.org/W2101285851","https://openalex.org/W2103972604","https://openalex.org/W2118550318","https://openalex.org/W2123921160","https://openalex.org/W2125276050","https://openalex.org/W2129812935","https://openalex.org/W2150856297","https://openalex.org/W2157785665","https://openalex.org/W2160547390","https://openalex.org/W2166049352","https://openalex.org/W2273827415","https://openalex.org/W2546307026","https://openalex.org/W2762869430","https://openalex.org/W2963505807","https://openalex.org/W3099013461","https://openalex.org/W4250955649","https://openalex.org/W4292363360","https://openalex.org/W6635552349","https://openalex.org/W6637386731","https://openalex.org/W6659319144","https://openalex.org/W6672484794","https://openalex.org/W6674642818","https://openalex.org/W6676727762"],"related_works":["https://openalex.org/W2151447942","https://openalex.org/W2611614995","https://openalex.org/W2900389025","https://openalex.org/W2148757832","https://openalex.org/W2293457016","https://openalex.org/W3107474891","https://openalex.org/W1914651075","https://openalex.org/W2510758617","https://openalex.org/W2540307000","https://openalex.org/W1982774199"],"abstract_inverted_index":{"Despite":[0],"the":[1,31,34,83,102,112,119,124,140,143,195,201],"fact":[2],"that":[3,93],"different":[4],"objects":[5],"possess":[6],"distinct":[7],"class-specific":[8,58],"features,":[9],"they":[10],"also":[11,153],"usually":[12],"share":[13],"common":[14,53],"patterns.":[15],"This":[16],"observation":[17],"has":[18],"been":[19],"exploited":[20],"partially":[21],"in":[22,123,142],"a":[23,42,50,75,88],"recently":[24],"proposed":[25],"dictionary":[26,67,77,107,127,183,203],"learning":[27,68,128,144,184,204],"framework":[28,69],"by":[29,38,73,171],"separating":[30],"particularity":[32],"and":[33,47,78,101,135,158,168,175],"commonality":[35],"(COPAR).":[36],"Inspired":[37],"this,":[39],"we":[40,86,115,131],"propose":[41],"novel":[43],"method":[44,199],"to":[45,105,138,156],"explicitly":[46],"simultaneously":[48],"learn":[49],"set":[51],"of":[52,163,180,197],"patterns":[54],"as":[55,57],"well":[56],"features":[59],"for":[60],"classification":[61],"with":[62,178],"more":[63],"intuitive":[64],"constraints.":[65],"Our":[66],"is":[70],"hence":[71],"characterized":[72],"both":[74],"shared":[76,84],"particular":[79,113],"(class-specific)":[80],"dictionaries.":[81],"For":[82,111],"dictionary,":[85],"enforce":[87],"low-rank":[89],"constraint,":[90],"i.e.,":[91],"claim":[92],"its":[94,147,159],"spanning":[95],"subspace":[96],"should":[97,108],"have":[98],"low":[99],"dimension":[100],"coefficients":[103],"corresponding":[104],"this":[106],"be":[109,154],"similar.":[110],"dictionaries,":[114],"impose":[116],"on":[117,188],"them":[118],"well-known":[120,182],"constraints":[121],"stated":[122],"Fisher":[125],"discrimination":[126],"(FDDL).":[129],"Furthermore,":[130],"develop":[132],"new":[133],"fast":[134],"accurate":[136],"algorithms":[137,151,165],"solve":[139],"subproblems":[141],"step,":[145],"accelerating":[146],"convergence.":[148],"The":[149,161],"said":[150],"could":[152],"applied":[155],"FDDL":[157],"extensions.":[160],"efficiencies":[162],"these":[164],"are":[166],"theoretically":[167],"experimentally":[169],"verified":[170],"comparing":[172],"their":[173],"complexities":[174],"running":[176],"time":[177],"those":[179],"other":[181],"methods.":[185,205],"Experimental":[186],"results":[187],"widely":[189],"used":[190],"image":[191],"data":[192],"sets":[193],"establish":[194],"advantages":[196],"our":[198],"over":[200],"state-of-the-art":[202]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":21},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":25},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":25},{"year":2019,"cited_by_count":26},{"year":2018,"cited_by_count":19},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2016-11-04T00:00:00"}