{"id":"https://openalex.org/W2557080744","doi":"https://doi.org/10.1109/mlsp.2016.7738833","title":"Random primal-dual proximal iterations for sparse multiclass SVM","display_name":"Random primal-dual proximal iterations for sparse multiclass SVM","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2557080744","doi":"https://doi.org/10.1109/mlsp.2016.7738833","mag":"2557080744"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2016.7738833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2016.7738833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)","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/A5057226360","display_name":"Giovanni Chierchia","orcid":"https://orcid.org/0000-0001-5899-689X"},"institutions":[{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]},{"id":"https://openalex.org/I4210152518","display_name":"Laboratoire d'Informatique Gaspard-Monge","ror":"https://ror.org/04t50yk91","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I142631665","https://openalex.org/I4210145102","https://openalex.org/I4210152518","https://openalex.org/I4210154111","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"G. Chierchia","raw_affiliation_strings":["LIGM UMR 8049, Universit\u00e9 Paris-Est, Noisy-le-Grand, France","Laboratoire d'Informatique Gaspard-Monge"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIGM UMR 8049, Universit\u00e9 Paris-Est, Noisy-le-Grand, France","institution_ids":["https://openalex.org/I2800365227","https://openalex.org/I4210152518"]},{"raw_affiliation_string":"Laboratoire d'Informatique Gaspard-Monge","institution_ids":["https://openalex.org/I4210152518"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074166239","display_name":"Nelly Pustelnik","orcid":"https://orcid.org/0000-0001-7310-1927"},"institutions":[{"id":"https://openalex.org/I113428412","display_name":"\u00c9cole Normale Sup\u00e9rieure de Lyon","ror":"https://ror.org/04zmssz18","country_code":"FR","type":"education","lineage":["https://openalex.org/I113428412","https://openalex.org/I203339264"]},{"id":"https://openalex.org/I4210096929","display_name":"Laboratoire de Physique de l'ENS de Lyon","ror":"https://ror.org/00w5ay796","country_code":"FR","type":"facility","lineage":["https://openalex.org/I100532134","https://openalex.org/I112936343","https://openalex.org/I113428412","https://openalex.org/I113428412","https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I203339264","https://openalex.org/I203339264","https://openalex.org/I203339264","https://openalex.org/I203339264","https://openalex.org/I203339264","https://openalex.org/I203339264","https://openalex.org/I4210096929","https://openalex.org/I4405259976","https://openalex.org/I4405263940","https://openalex.org/I48430043"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"N. Pustelnik","raw_affiliation_strings":["Ens de Lyon, Laboratoire de Physique, Lyon, France","Laboratoire de Physique de l'ENS Lyon"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ens de Lyon, Laboratoire de Physique, Lyon, France","institution_ids":["https://openalex.org/I113428412","https://openalex.org/I4210096929"]},{"raw_affiliation_string":"Laboratoire de Physique de l'ENS Lyon","institution_ids":["https://openalex.org/I113428412","https://openalex.org/I4210096929"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112480949","display_name":"J.-C. Pesquet","orcid":null},"institutions":[{"id":"https://openalex.org/I2800365227","display_name":"Paris-Est Sup","ror":"https://ror.org/0268ecp52","country_code":"FR","type":"education","lineage":["https://openalex.org/I2800365227"]},{"id":"https://openalex.org/I4210152518","display_name":"Laboratoire d'Informatique Gaspard-Monge","ror":"https://ror.org/04t50yk91","country_code":"FR","type":"facility","lineage":["https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I142631665","https://openalex.org/I4210145102","https://openalex.org/I4210152518","https://openalex.org/I4210154111","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"J.-C. Pesquet","raw_affiliation_strings":["LIGM UMR 8049, Universit\u00e9 Paris-Est, Noisy-le-Grand, France","Laboratoire d'Informatique Gaspard-Monge","Organ Modeling through Extraction, Representation and Understanding of Medical Image Content"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIGM UMR 8049, Universit\u00e9 Paris-Est, Noisy-le-Grand, France","institution_ids":["https://openalex.org/I2800365227","https://openalex.org/I4210152518"]},{"raw_affiliation_string":"Laboratoire d'Informatique Gaspard-Monge","institution_ids":["https://openalex.org/I4210152518"]},{"raw_affiliation_string":"Organ Modeling through Extraction, Representation and Understanding of Medical Image Content","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9954000115394592,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.994700014591217,"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/coordinate-descent","display_name":"Coordinate descent","score":0.8341709971427917},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7226992249488831},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6775833964347839},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.6460692882537842},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6232770085334778},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6103901863098145},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5706446766853333},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5387061834335327},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.46842849254608154},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.45349985361099243},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.44515615701675415},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.37960073351860046},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3605092763900757},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20599707961082458},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.0822506844997406}],"concepts":[{"id":"https://openalex.org/C157553263","wikidata":"https://www.wikidata.org/wiki/Q5168004","display_name":"Coordinate descent","level":2,"score":0.8341709971427917},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7226992249488831},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6775833964347839},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.6460692882537842},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6232770085334778},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6103901863098145},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5706446766853333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5387061834335327},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.46842849254608154},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45349985361099243},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.44515615701675415},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.37960073351860046},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3605092763900757},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20599707961082458},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.0822506844997406},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/mlsp.2016.7738833","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2016.7738833","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-01419697v1","is_oa":false,"landing_page_url":"https://hal.science/hal-01419697","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE International Workshop on Machine Learning for Signal Processing, Sep 2016, Vietri sul Mare, Italy. ⟨10.1109/MLSP.2016.7738833⟩","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W27230717","https://openalex.org/W960403429","https://openalex.org/W1493782845","https://openalex.org/W1749992802","https://openalex.org/W1764447967","https://openalex.org/W1774344329","https://openalex.org/W1826065584","https://openalex.org/W1877183374","https://openalex.org/W1946620893","https://openalex.org/W2072925128","https://openalex.org/W2077659054","https://openalex.org/W2093545205","https://openalex.org/W2096291962","https://openalex.org/W2106398669","https://openalex.org/W2112796928","https://openalex.org/W2117686388","https://openalex.org/W2119821739","https://openalex.org/W2138019504","https://openalex.org/W2157791002","https://openalex.org/W2159514083","https://openalex.org/W2161278885","https://openalex.org/W2165966284","https://openalex.org/W2180180824","https://openalex.org/W2272509765","https://openalex.org/W2521574096","https://openalex.org/W2594827121","https://openalex.org/W2964168652","https://openalex.org/W3004533406","https://openalex.org/W3025107628","https://openalex.org/W3104533819","https://openalex.org/W4205213118","https://openalex.org/W4229650096","https://openalex.org/W4239510810","https://openalex.org/W4244393449","https://openalex.org/W4288108337","https://openalex.org/W6601118098","https://openalex.org/W6625221448","https://openalex.org/W6635366801","https://openalex.org/W6637196363","https://openalex.org/W6637938255","https://openalex.org/W6638358837","https://openalex.org/W6682953061","https://openalex.org/W6694186548"],"related_works":["https://openalex.org/W2117229703","https://openalex.org/W3120756218","https://openalex.org/W4286693783","https://openalex.org/W3175914740","https://openalex.org/W4294982320","https://openalex.org/W3034587794","https://openalex.org/W3210805454","https://openalex.org/W2020098476","https://openalex.org/W2356529274","https://openalex.org/W2953288298"],"abstract_inverted_index":{"Sparsity-inducing":[0],"penalties":[1],"are":[2],"useful":[3],"tools":[4],"in":[5,90],"variational":[6],"methods":[7],"for":[8,20,75],"machine":[9],"learning.":[10],"In":[11],"this":[12],"paper,":[13],"we":[14],"propose":[15],"two":[16],"block-coordinate":[17,94],"descent":[18],"strategies":[19],"learning":[21],"a":[22,34,105],"sparse":[23],"multiclass":[24],"support":[25],"vector":[26],"machine.":[27],"The":[28,96],"first":[29],"one":[30,47],"works":[31],"by":[32,66],"selecting":[33],"subset":[35],"of":[36,79,85,93,98,107],"features":[37],"to":[38,62],"be":[39,58],"updated":[40],"at":[41],"each":[42],"iteration,":[43],"while":[44],"the":[45,49,52,63,76,83,87,91,99],"second":[46],"performs":[48],"selection":[50],"among":[51],"training":[53],"samples.":[54],"These":[55],"algorithms":[56,101],"can":[57],"efficiently":[59],"implemented":[60],"thanks":[61],"flexibility":[64],"offered":[65],"recent":[67],"randomized":[68],"primal-dual":[69,88],"proximal":[70],"methods.":[71],"Experiments":[72],"carried":[73],"out":[74],"supervised":[77],"classification":[78,111],"handwritten":[80],"digits":[81],"demonstrate":[82],"interest":[84],"considering":[86],"approach":[89],"context":[92],"descent.":[95],"efficiency":[97],"proposed":[100],"is":[102],"assessed":[103],"through":[104],"comparison":[106],"execution":[108],"times":[109],"and":[110],"errors.":[112]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-25T15:57:00.446498","created_date":"2025-10-10T00:00:00"}