{"id":"https://openalex.org/W2978758977","doi":"https://doi.org/10.1109/ijcnn.2019.8852179","title":"Supervised Kernel Transform Learning","display_name":"Supervised Kernel Transform Learning","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978758977","doi":"https://doi.org/10.1109/ijcnn.2019.8852179","mag":"2978758977"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852179","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/A5061490660","display_name":"Jyoti Maggu","orcid":"https://orcid.org/0000-0002-9729-5356"},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Jyoti Maggu","raw_affiliation_strings":["Indraprastha Institute of Information Technology, Delhi, India"],"affiliations":[{"raw_affiliation_string":"Indraprastha Institute of Information Technology, Delhi, India","institution_ids":["https://openalex.org/I119939252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020310463","display_name":"Angshul Majumdar","orcid":"https://orcid.org/0000-0002-1065-3000"},"institutions":[{"id":"https://openalex.org/I119939252","display_name":"Indraprastha Institute of Information Technology Delhi","ror":"https://ror.org/03vfp4g33","country_code":"IN","type":"education","lineage":["https://openalex.org/I119939252"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Angshul Majumdar","raw_affiliation_strings":["Indraprastha Institute of Information Technology, Delhi, India"],"affiliations":[{"raw_affiliation_string":"Indraprastha Institute of Information Technology, Delhi, India","institution_ids":["https://openalex.org/I119939252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5061490660"],"corresponding_institution_ids":["https://openalex.org/I119939252"],"apc_list":null,"apc_paid":null,"fwci":0.3882,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.6065835,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"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":0.9997000098228455,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9976999759674072,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.662665605545044},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6522520184516907},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5632843375205994},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5193808674812317},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.504128098487854},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5006954669952393},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4728662669658661},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4683648347854614},{"id":"https://openalex.org/keywords/discrete-cosine-transform","display_name":"Discrete cosine transform","score":0.4373655915260315},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4367048740386963},{"id":"https://openalex.org/keywords/similarity-learning","display_name":"Similarity learning","score":0.4201200008392334},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41937577724456787},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27378541231155396}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.662665605545044},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6522520184516907},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5632843375205994},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5193808674812317},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.504128098487854},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5006954669952393},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4728662669658661},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4683648347854614},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.4373655915260315},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4367048740386963},{"id":"https://openalex.org/C2779597229","wikidata":"https://www.wikidata.org/wiki/Q17146505","display_name":"Similarity learning","level":3,"score":0.4201200008392334},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41937577724456787},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27378541231155396},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852179","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":[{"score":0.5,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W4919037","https://openalex.org/W1902027874","https://openalex.org/W1927762903","https://openalex.org/W1979646128","https://openalex.org/W1986611834","https://openalex.org/W1989833002","https://openalex.org/W1994281301","https://openalex.org/W1994670735","https://openalex.org/W2002932002","https://openalex.org/W2011464887","https://openalex.org/W2027805700","https://openalex.org/W2077964948","https://openalex.org/W2093238275","https://openalex.org/W2095705004","https://openalex.org/W2097200430","https://openalex.org/W2099321050","https://openalex.org/W2099749431","https://openalex.org/W2115429828","https://openalex.org/W2115706991","https://openalex.org/W2117882039","https://openalex.org/W2118057659","https://openalex.org/W2141039087","https://openalex.org/W2145889472","https://openalex.org/W2146842127","https://openalex.org/W2149861038","https://openalex.org/W2153663612","https://openalex.org/W2154672679","https://openalex.org/W2160547390","https://openalex.org/W2163112044","https://openalex.org/W2163398148","https://openalex.org/W2189938900","https://openalex.org/W2254969944","https://openalex.org/W2289846183","https://openalex.org/W2291961022","https://openalex.org/W2330290358","https://openalex.org/W2343962831","https://openalex.org/W2390063609","https://openalex.org/W2547190925","https://openalex.org/W2556806401","https://openalex.org/W2579582149","https://openalex.org/W2585132294","https://openalex.org/W2753163043","https://openalex.org/W2754803257","https://openalex.org/W2963958000","https://openalex.org/W3100063120","https://openalex.org/W6600213771","https://openalex.org/W6674330103"],"related_works":["https://openalex.org/W1916685473","https://openalex.org/W2055682261","https://openalex.org/W1993363272","https://openalex.org/W2186390138","https://openalex.org/W2060035984","https://openalex.org/W2790129917","https://openalex.org/W2992856432","https://openalex.org/W2100638064","https://openalex.org/W2127787376","https://openalex.org/W2766535710"],"abstract_inverted_index":{"This":[0],"work":[1,121],"introduces":[2,123],"certain":[3],"supervised":[4,114],"formulations":[5],"for":[6],"transform":[7,110,133],"learning.":[8,17],"Transform":[9],"learning":[10,111,134],"is":[11,29,84],"the":[12,64,85,96,100,104,132,154],"analysis":[13],"equivalent":[14],"of":[15,21,38,51,63,74],"dictionary":[16],"Four":[18],"different":[19,75,143],"types":[20],"supervision":[22],"penalties":[23],"are":[24],"proposed.":[25],"The":[26,41,58,81],"first":[27,105],"one":[28,43],"class-sparsity,":[30],"which":[31,90],"imposes":[32,44],"common":[33],"sparse":[34],"support":[35],"within":[36],"representations":[37],"each":[39,70],"class.":[40],"second":[42],"similarity":[45],"among":[46],"intra-class":[47],"features":[48,62,73],"in":[49],"terms":[50],"a":[52,92],"low-rank":[53],"constraint":[54],"(high":[55],"cosine":[56],"similarity).":[57],"third":[59],"penalty":[60],"enforces":[61],"same":[65],"class":[66,101],"to":[67,77,99],"be":[68,78,117],"nearby":[69],"other":[71],"and":[72,129,148],"classes":[76],"far":[79],"apart.":[80],"final":[82],"formulation":[83,89],"well":[86],"known":[87],"label-consistency":[88],"learns":[91],"linear":[93],"map":[94],"from":[95],"feature":[97],"space":[98],"targets.":[102],"For":[103],"time,":[106],"we":[107],"show":[108],"how":[109],"(and":[112],"its":[113],"versions":[115],"can":[116],"kernelized).":[118],"Finally":[119],"this":[120],"also":[122],"stochastic":[124],"regularization":[125],"techniques":[126],"like":[127],"DropOut":[128],"DropConnect":[130],"into":[131],"formulation.":[135],"Experiments":[136],"have":[137],"been":[138],"carried":[139],"out":[140],"on":[141],"two":[142],"problems":[144],"-":[145],"computer":[146],"vision":[147],"biomedial":[149],"signal":[150],"analysis.":[151],"In":[152],"both":[153],"problems,":[155],"our":[156],"method":[157],"excels":[158],"over":[159],"all":[160],"existing":[161],"ones.":[162]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-07T14:57:38.498316","created_date":"2025-10-10T00:00:00"}
