{"id":"https://openalex.org/W2978598095","doi":"https://doi.org/10.1109/ijcnn.2019.8852201","title":"Coupled Dictionary Learning for Multi-label Embedding","display_name":"Coupled Dictionary Learning for Multi-label Embedding","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978598095","doi":"https://doi.org/10.1109/ijcnn.2019.8852201","mag":"2978598095"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852201","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852201","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-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/A5051284775","display_name":"Sijia Niu","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sijia Niu","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100461059","display_name":"Qian Xu","orcid":"https://orcid.org/0000-0001-5690-0583"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qian Xu","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006952581","display_name":"Pengfei Zhu","orcid":"https://orcid.org/0000-0002-4310-9140"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengfei Zhu","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056686459","display_name":"Qinghua Hu","orcid":"https://orcid.org/0000-0001-7765-8095"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinghua Hu","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101971392","display_name":"Hong Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Shi","raw_affiliation_strings":["College of Intelligence and Computing, Tianjin University, Tianjin, China"],"affiliations":[{"raw_affiliation_string":"College of Intelligence and Computing, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5051284775"],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":0.28,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66137926,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"17","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9901999831199646,"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/T10057","display_name":"Face and Expression Recognition","score":0.9818999767303467,"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/interpretability","display_name":"Interpretability","score":0.7996946573257446},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7462711930274963},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6814401149749756},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6193649172782898},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5900324583053589},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5615778565406799},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5433114767074585},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5417801141738892},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.530906081199646},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.4950036406517029},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4772919714450836},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.46043887734413147},{"id":"https://openalex.org/keywords/multi-label-classification","display_name":"Multi-label classification","score":0.4580923020839691},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.451107382774353},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.44888487458229065},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.43475541472435},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1207423210144043}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7996946573257446},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7462711930274963},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6814401149749756},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6193649172782898},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5900324583053589},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5615778565406799},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5433114767074585},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5417801141738892},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.530906081199646},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.4950036406517029},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4772919714450836},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.46043887734413147},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.4580923020839691},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.451107382774353},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.44888487458229065},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.43475541472435},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1207423210144043},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/ijcnn.2019.8852201","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852201","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7200000286102295,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W193151672","https://openalex.org/W1546425806","https://openalex.org/W1666447063","https://openalex.org/W1877469910","https://openalex.org/W1890834058","https://openalex.org/W1972490990","https://openalex.org/W2005876975","https://openalex.org/W2010070459","https://openalex.org/W2027869746","https://openalex.org/W2053463056","https://openalex.org/W2063978378","https://openalex.org/W2086962710","https://openalex.org/W2088254198","https://openalex.org/W2100556411","https://openalex.org/W2114315281","https://openalex.org/W2114393161","https://openalex.org/W2115119296","https://openalex.org/W2116878278","https://openalex.org/W2118712128","https://openalex.org/W2120824855","https://openalex.org/W2133510502","https://openalex.org/W2136484596","https://openalex.org/W2136995369","https://openalex.org/W2141282920","https://openalex.org/W2145908112","https://openalex.org/W2153677638","https://openalex.org/W2164278908","https://openalex.org/W2164308541","https://openalex.org/W2166912588","https://openalex.org/W2255761615","https://openalex.org/W2296399167","https://openalex.org/W2951238624","https://openalex.org/W2953000611","https://openalex.org/W3140910462","https://openalex.org/W4292363360","https://openalex.org/W6607898275","https://openalex.org/W6632714361","https://openalex.org/W6637249095","https://openalex.org/W6646076250","https://openalex.org/W6677150689","https://openalex.org/W6677758222","https://openalex.org/W6680361382","https://openalex.org/W6681897715","https://openalex.org/W6764172607","https://openalex.org/W6792614579"],"related_works":["https://openalex.org/W2509955295","https://openalex.org/W1987225540","https://openalex.org/W2363993830","https://openalex.org/W1778286912","https://openalex.org/W2561456314","https://openalex.org/W2249096836","https://openalex.org/W1992008660","https://openalex.org/W2116933539","https://openalex.org/W1587263836","https://openalex.org/W4245251483"],"abstract_inverted_index":{"With":[0],"the":[1,11,37,54,65,95,114,120,129,134,147],"booming":[2],"of":[3,14,27,34,67,97,136,149],"social":[4],"networks,":[5],"such":[6],"as":[7,105],"Facebook":[8],"and":[9,103,124,139],"Flickr,":[10],"candidate":[12],"labels":[13,35],"an":[15],"instance":[16],"can":[17],"be":[18],"numerous.":[19],"Hence,":[20],"traditional":[21],"multi-label":[22],"learning":[23,70],"algorithms":[24],"are":[25],"out":[26],"capability":[28],"to":[29,93,112],"handle":[30],"a":[31,59,82],"large":[32],"quantity":[33],"for":[36,89],"unaffordable":[38],"time":[39],"complexity.":[40],"To":[41],"alleviate":[42],"this":[43,78],"problem,":[44],"label":[45,56,104,125],"space":[46,57,117],"dimension":[47],"reduction":[48],"(LSDR)":[49],"is":[50,110],"proposed":[51,81],"by":[52,64],"transforming":[53],"original":[55],"into":[58],"lower":[60],"dimensional":[61],"one.":[62],"Inspired":[63],"effectiveness":[66,148],"coupled":[68,106],"dictionary":[69],"(CDL)":[71],"in":[72,77],"dealing":[73],"with":[74],"cross-modal":[75],"data,":[76],"paper,":[79],"we":[80],"novel":[83],"algorithm":[84],"named":[85],"Coupled":[86],"Dictionary":[87],"Learning":[88],"Multi-label":[90],"Embedding":[91],"(ML-CDL)":[92],"track":[94],"problem":[96],"LSDR.":[98],"We":[99],"novelly":[100],"treat":[101],"feature":[102,123],"domains.":[107],"Then":[108],"CDL":[109],"utilized":[111],"generate":[113],"low-dimensional":[115],"latent":[116],"that":[118],"leverages":[119],"information":[121],"between":[122],"spaces.":[126],"In":[127],"particular,":[128],"sparse":[130],"representation":[131],"coefficients":[132],"embody":[133],"properties":[135],"interpretability,":[137],"discriminability":[138],"sparsity.":[140],"Experimental":[141],"results":[142],"on":[143],"benchmark":[144],"datasets":[145],"demonstrate":[146],"our":[150],"algorithm.":[151]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
