{"id":"https://openalex.org/W2534454225","doi":"https://doi.org/10.1109/globalsip.2013.6736963","title":"Dictionary learning via projected maximal exploration","display_name":"Dictionary learning via projected maximal exploration","publication_year":2013,"publication_date":"2013-12-01","ids":{"openalex":"https://openalex.org/W2534454225","doi":"https://doi.org/10.1109/globalsip.2013.6736963","mag":"2534454225"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2013.6736963","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2013.6736963","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Global Conference on Signal and Information Processing","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/A5017886311","display_name":"Boris Mailh\u00e9","orcid":null},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Boris Mailhe","raw_affiliation_strings":["School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066967599","display_name":"Mark D. Plumbley","orcid":"https://orcid.org/0000-0002-9708-1075"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mark D. Plumbley","raw_affiliation_strings":["School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom"],"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom","institution_ids":["https://openalex.org/I166337079"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5017886311"],"corresponding_institution_ids":["https://openalex.org/I166337079"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29310472,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"54","issue":null,"first_page":"626","last_page":"626"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.992900013923645,"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"}},{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.987500011920929,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.8050848841667175},{"id":"https://openalex.org/keywords/monotonic-function","display_name":"Monotonic function","score":0.7053093314170837},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.615581750869751},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.6093970537185669},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5706862807273865},{"id":"https://openalex.org/keywords/proximal-gradient-methods","display_name":"Proximal Gradient Methods","score":0.5438999533653259},{"id":"https://openalex.org/keywords/gradient-method","display_name":"Gradient method","score":0.5005946159362793},{"id":"https://openalex.org/keywords/stochastic-gradient-descent","display_name":"Stochastic gradient descent","score":0.4159989655017853},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3352237343788147},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.32343143224716187},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2926373779773712},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.1658032238483429},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.09377577900886536}],"concepts":[{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.8050848841667175},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.7053093314170837},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.615581750869751},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.6093970537185669},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5706862807273865},{"id":"https://openalex.org/C10494615","wikidata":"https://www.wikidata.org/wiki/Q17086765","display_name":"Proximal Gradient Methods","level":4,"score":0.5438999533653259},{"id":"https://openalex.org/C115680565","wikidata":"https://www.wikidata.org/wiki/Q5977448","display_name":"Gradient method","level":2,"score":0.5005946159362793},{"id":"https://openalex.org/C206688291","wikidata":"https://www.wikidata.org/wiki/Q7617819","display_name":"Stochastic gradient descent","level":3,"score":0.4159989655017853},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3352237343788147},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32343143224716187},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2926373779773712},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.1658032238483429},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.09377577900886536},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/globalsip.2013.6736963","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2013.6736963","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Global Conference on Signal and Information Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:alma.44SUR_INST:11139622100002346","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4210197018","display_name":"View","issn_l":"2688-268X","issn":["2688-268X","2688-3988"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"","raw_type":""},{"id":"pmh:oai:epubs.surrey.ac.uk:838867","is_oa":false,"landing_page_url":"http://epubs.surrey.ac.uk/838867/1/MailhePlumbley13-globalsip_accepted_notice.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400680","display_name":"Surrey Research Insight Open Access (The University of Surrey)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I28290843","host_organization_name":"University of Surrey","host_organization_lineage":["https://openalex.org/I28290843"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference or Workshop Item"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.6299999952316284}],"awards":[{"id":"https://openalex.org/G6723506329","display_name":null,"funder_award_id":"EP/G007144/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"}],"funders":[{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W67201968","https://openalex.org/W2115429828","https://openalex.org/W2160547390"],"related_works":["https://openalex.org/W2811217697","https://openalex.org/W1977633006","https://openalex.org/W4385644471","https://openalex.org/W4320495396","https://openalex.org/W4295266778","https://openalex.org/W2564172750","https://openalex.org/W2041341337","https://openalex.org/W2075334256","https://openalex.org/W2372778757","https://openalex.org/W2026452100"],"abstract_inverted_index":{"This":[0],"work":[1],"presents":[2],"a":[3,24,28,50],"geometrical":[4],"analysis":[5],"of":[6,20],"the":[7,18,21,34,41,64,70,76,80],"Large":[8],"Step":[9],"Gradient":[10],"Descent":[11],"(LGD)":[12],"dictionary":[13,22],"learning":[14],"algorithm.":[15],"LGD":[16,71],"updates":[17],"atoms":[19],"using":[23],"gradient":[25,44],"step":[26,29,36,43,53,83],"with":[27],"size":[30],"equal":[31],"to":[32,87],"twice":[33],"optimal":[35],"size.":[37],"We":[38,66],"show":[39,68],"that":[40,69],"large":[42],"descent":[45],"can":[46],"be":[47],"understood":[48],"as":[49,57,60],"maximal":[51],"exploration":[52],"where":[54],"one":[55],"goes":[56],"far":[58],"away":[59],"possible":[61],"without":[62],"increasing":[63],"error.":[65],"also":[67],"iteration":[72],"is":[73,84],"monotonic":[74],"when":[75],"algorithm":[77],"used":[78],"for":[79],"sparse":[81],"approximation":[82],"close":[85],"enough":[86],"orthogonal.":[88]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}