{"id":"https://openalex.org/W2982640936","doi":"https://doi.org/10.48550/arxiv.1711.03638","title":"Provably Accurate Double-Sparse Coding","display_name":"Provably Accurate Double-Sparse Coding","publication_year":2017,"publication_date":"2017-11-09","ids":{"openalex":"https://openalex.org/W2982640936","doi":"https://doi.org/10.48550/arxiv.1711.03638","mag":"2982640936"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1711.03638","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1711.03638","pdf_url":"https://arxiv.org/pdf/1711.03638","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1711.03638","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029385096","display_name":"Thanh V. Nguyen","orcid":"https://orcid.org/0000-0003-1576-5420","observed_orcids":["https://orcid.org/0000-0003-1576-5420"]},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nguyen, Thanh V.","raw_affiliation_strings":["Iowa State University , ECE Department","Iowa State University\u2028"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Iowa State University , ECE Department","institution_ids":["https://openalex.org/I173911158"]},{"raw_affiliation_string":"Iowa State University\u2028","institution_ids":["https://openalex.org/I173911158"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wong, Raymond K. W.","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]},{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wong, Raymond K. W.","raw_affiliation_strings":["Iowa State University , ECE Department","Texas A&M University , Statistics Department","\u2020Texas A&M University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Iowa State University , ECE Department","institution_ids":["https://openalex.org/I173911158"]},{"raw_affiliation_string":"Texas A&M University , Statistics Department","institution_ids":["https://openalex.org/I91045830"]},{"raw_affiliation_string":"\u2020Texas A&M University","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066142047","display_name":"Chinmay Hegde","orcid":"https://orcid.org/0000-0003-4574-8066","observed_orcids":["https://orcid.org/0000-0003-4574-8066"]},"institutions":[{"id":"https://openalex.org/I173911158","display_name":"Iowa State University","ror":"https://ror.org/04rswrd78","country_code":"US","type":"education","lineage":["https://openalex.org/I173911158"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hegde, Chinmay","raw_affiliation_strings":["Iowa State University , ECE Department"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Iowa State University , ECE Department","institution_ids":["https://openalex.org/I173911158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"20","issue":"141","first_page":"1","last_page":"43"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9990000128746033,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9990000128746033,"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/T10136","display_name":"Statistical Methods and Inference","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11210","display_name":"Mathematical Analysis and Transform Methods","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/sparse-coding","display_name":"sparse coding","score":0.8700000047683716},{"id":"https://openalex.org/keywords/sample-complexity","display_name":"sample complexity","score":0.7419999837875366},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.6859999895095825},{"id":"https://openalex.org/keywords/statistical-guarantees","display_name":"statistical guarantees","score":0.7229999899864197},{"id":"https://openalex.org/keywords/neural-architecture","display_name":"neural architecture","score":0.527999997138977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7991613149642944},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.7197738885879517},{"id":"https://openalex.org/C96147967","wikidata":"https://www.wikidata.org/wiki/Q190686","display_name":"Subroutine","level":2,"score":0.6053698658943176},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.5581903457641602},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.4972539246082306},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4644681513309479},{"id":"https://openalex.org/C154771677","wikidata":"https://www.wikidata.org/wiki/Q17098361","display_name":"K-SVD","level":3,"score":0.4446669816970825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4378221035003662},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4342373013496399},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.42961585521698},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.41455787420272827},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.352720707654953},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14364755153656006},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:1711.03638","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1711.03638","pdf_url":"https://arxiv.org/pdf/1711.03638","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.1711.03638","doi":"https://doi.org/10.48550/arxiv.1711.03638","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1711.03638","pdf_url":null,"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,"listed_in":[],"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},{"id":"mag:2982640936","doi":null,"is_oa":false,"landing_page_url":"https://jmlr.org/papers/volume20/17-728/17-728.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","ccf-a","cwts-core","dongbi-a","jufo-3","ki-jl-2","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Machine Learning Research","raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1711.03638","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1711.03638","pdf_url":"https://arxiv.org/pdf/1711.03638","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3112118179","display_name":"Collaborative Research: New Directions in Multidimensional and Multivariate Functional Data Analysis","funder_award_id":"1612985","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5901857127","display_name":"CRII: CIF: Towards Linear-Time Computation of Structured Data Representations","funder_award_id":"1566281","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2982640936.pdf","grobid_xml":"https://content.openalex.org/works/W2982640936.grobid-xml"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W2287998125","https://openalex.org/W2538110817","https://openalex.org/W2966802804"],"related_works":["https://openalex.org/W2963571266","https://openalex.org/W2105459687","https://openalex.org/W3036637556","https://openalex.org/W3153711428","https://openalex.org/W2122508088","https://openalex.org/W7095644327","https://openalex.org/W2099321050","https://openalex.org/W1489635965","https://openalex.org/W1933990309","https://openalex.org/W2005876975"],"abstract_inverted_index":{"Sparse":[0],"coding":[1,99,145],"is":[2,22,40,58,78],"a":[3,32,37,82,87,94],"crucial":[4],"subroutine":[5],"in":[6,171,175],"algorithms":[7],"for":[8,97,129,143],"various":[9],"signal":[10],"processing,":[11],"deep":[12],"learning,":[13],"and":[14,44,86,116,121],"other":[15],"machine":[16],"learning":[17],"applications.":[18,177],"The":[19],"central":[20],"goal":[21],"to":[23,104],"learn":[24],"an":[25],"overcomplete":[26],"dictionary":[27,49,76],"that":[28,41,100,146,164],"can":[29,50,101,167],"sparsely":[30],"represent":[31],"given":[33],"input":[34],"dataset.":[35],"However,":[36],"key":[38],"challenge":[39],"storage,":[42],"transmission,":[43],"processing":[45],"of":[46,81],"the":[47,55,65,75,79,138],"learned":[48],"be":[51,102,169],"untenably":[52],"high":[53],"if":[54],"data":[56],"dimension":[57],"high.":[59],"In":[60],"this":[61],"paper,":[62],"we":[63,92,111,152],"consider":[64],"double-sparsity":[66],"model":[67],"introduced":[68],"by":[69],"Rubinstein":[70],"et":[71],"al.":[72],"(2010b)":[73],"where":[74],"itself":[77],"product":[80],"fixed,":[83],"known":[84],"basis":[85],"data-adaptive":[88],"sparse":[89,130],"component.":[90],"First,":[91],"introduce":[93],"simple":[95],"algorithm":[96,142],"double-sparse":[98,144],"amenable":[103],"efficient":[105,141],"implementation":[106],"via":[107,156],"neural":[108],"architectures.":[109],"Second,":[110],"theoretically":[112],"analyze":[113],"its":[114],"performance":[115],"demonstrate":[117],"asymptotic":[118],"sample":[119],"complexity":[120],"running":[122],"time":[123],"benefits":[124],"over":[125],"existing":[126],"(provable)":[127],"approaches":[128],"coding.":[131],"To":[132],"our":[133,135,154,165],"knowledge,":[134],"work":[136],"introduces":[137],"first":[139],"computationally":[140],"enjoys":[147],"rigorous":[148],"statistical":[149],"guarantees.":[150],"Finally,":[151],"support":[153],"analysis":[155],"several":[157],"numerical":[158],"experiments":[159],"on":[160],"simulated":[161],"data,":[162],"confirming":[163],"method":[166],"indeed":[168],"useful":[170],"problem":[172],"sizes":[173],"encountered":[174],"practical":[176]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2019-11-08T00:00:00"}
