{"id":"https://openalex.org/W2096398081","doi":"https://doi.org/10.1109/tit.2014.2313085","title":"Lossy Compression via Sparse Linear Regression: Performance Under Minimum-Distance Encoding","display_name":"Lossy Compression via Sparse Linear Regression: Performance Under Minimum-Distance Encoding","publication_year":2014,"publication_date":"2014-05-16","ids":{"openalex":"https://openalex.org/W2096398081","doi":"https://doi.org/10.1109/tit.2014.2313085","mag":"2096398081"},"language":"en","primary_location":{"id":"doi:10.1109/tit.2014.2313085","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2014.2313085","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1202.0840","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ramji Venkataramanan","orcid":null},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ramji Venkataramanan","raw_affiliation_strings":["Department of Engineering, University of Cambridge, Cambridge, U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Engineering, University of Cambridge, Cambridge, U.K","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Antony Joseph","orcid":null},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Antony Joseph","raw_affiliation_strings":["Department of Statistics, University of California at Berkeley, Berkeley, CA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of California at Berkeley, Berkeley, CA, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":null,"display_name":"Sekhar Tatikonda","orcid":null},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sekhar Tatikonda","raw_affiliation_strings":["Department of Electrical Engineering, Yale University, New Haven, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Yale University, New Haven, CT, USA","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4144,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.68980747,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"60","issue":"6","first_page":"3254","last_page":"3264"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10964","display_name":"Wireless Communication Security Techniques","score":0.39469999074935913,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10964","display_name":"Wireless Communication Security Techniques","score":0.39469999074935913,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.29120001196861267,"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/T11321","display_name":"Error Correcting Code Techniques","score":0.09000000357627869,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/lossy-compression","display_name":"Lossy compression","score":0.5974000096321106},{"id":"https://openalex.org/keywords/codebook","display_name":"Codebook","score":0.52920001745224},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5133000016212463},{"id":"https://openalex.org/keywords/additive-white-gaussian-noise","display_name":"Additive white Gaussian noise","score":0.49799999594688416},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.48890000581741333},{"id":"https://openalex.org/keywords/distortion","display_name":"Distortion (music)","score":0.44940000772476196},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.4341999888420105},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.4284000098705292},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.38960000872612},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.3734000027179718}],"concepts":[{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6187999844551086},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.5974000096321106},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5903000235557556},{"id":"https://openalex.org/C127759330","wikidata":"https://www.wikidata.org/wiki/Q637416","display_name":"Codebook","level":2,"score":0.52920001745224},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5133000016212463},{"id":"https://openalex.org/C169334058","wikidata":"https://www.wikidata.org/wiki/Q353292","display_name":"Additive white Gaussian noise","level":3,"score":0.49799999594688416},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.48890000581741333},{"id":"https://openalex.org/C126780896","wikidata":"https://www.wikidata.org/wiki/Q899871","display_name":"Distortion (music)","level":4,"score":0.44940000772476196},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.4341999888420105},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.4284000098705292},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.38960000872612},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.3734000027179718},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3580000102519989},{"id":"https://openalex.org/C73150493","wikidata":"https://www.wikidata.org/wiki/Q853922","display_name":"Hamming code","level":4,"score":0.3343999981880188},{"id":"https://openalex.org/C2400350","wikidata":"https://www.wikidata.org/wiki/Q1752667","display_name":"Linear code","level":4,"score":0.3305000066757202},{"id":"https://openalex.org/C122044880","wikidata":"https://www.wikidata.org/wiki/Q5498822","display_name":"Ergodic theory","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C51267290","wikidata":"https://www.wikidata.org/wiki/Q5527848","display_name":"Gaussian random field","level":4,"score":0.3181999921798706},{"id":"https://openalex.org/C200801453","wikidata":"https://www.wikidata.org/wiki/Q5283181","display_name":"Distributed source coding","level":4,"score":0.31369999051094055},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.31310001015663147},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C27753989","wikidata":"https://www.wikidata.org/wiki/Q284885","display_name":"Superposition principle","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.301800012588501},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C64185310","wikidata":"https://www.wikidata.org/wiki/Q843483","display_name":"Rate\u2013distortion theory","level":3,"score":0.2921999990940094},{"id":"https://openalex.org/C7218915","wikidata":"https://www.wikidata.org/wiki/Q1054475","display_name":"Gaussian function","level":3,"score":0.28110000491142273},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.27970001101493835},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.2759999930858612},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C64812099","wikidata":"https://www.wikidata.org/wiki/Q176604","display_name":"Random matrix","level":3,"score":0.26750001311302185},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.26570001244544983},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C203233044","wikidata":"https://www.wikidata.org/wiki/Q5264358","display_name":"Design matrix","level":3,"score":0.25679999589920044},{"id":"https://openalex.org/C112633086","wikidata":"https://www.wikidata.org/wiki/Q381287","display_name":"White noise","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tit.2014.2313085","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tit.2014.2313085","pdf_url":null,"source":{"id":"https://openalex.org/S4502562","display_name":"IEEE Transactions on Information Theory","issn_l":"0018-9448","issn":["0018-9448","1557-9654"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Information Theory","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1202.0840","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1202.0840","pdf_url":"https://arxiv.org/pdf/1202.0840","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1202.0840","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1202.0840","pdf_url":"https://arxiv.org/pdf/1202.0840","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8838449627","display_name":null,"funder_award_id":"CCF-1017744","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W1484170624","https://openalex.org/W1660164274","https://openalex.org/W1674742109","https://openalex.org/W1971079746","https://openalex.org/W2027827150","https://openalex.org/W2037842665","https://openalex.org/W2039957496","https://openalex.org/W2052690615","https://openalex.org/W2075002608","https://openalex.org/W2090887848","https://openalex.org/W2104357403","https://openalex.org/W2111992817","https://openalex.org/W2123128455","https://openalex.org/W2127271355","https://openalex.org/W2129131372","https://openalex.org/W2137049264","https://openalex.org/W2140752095","https://openalex.org/W2141556672","https://openalex.org/W2144452965","https://openalex.org/W2145901379","https://openalex.org/W2145953267","https://openalex.org/W2146124189","https://openalex.org/W2150395490","https://openalex.org/W2151693816","https://openalex.org/W2158137498","https://openalex.org/W2166171880","https://openalex.org/W2166347904","https://openalex.org/W2166571310","https://openalex.org/W2478722229","https://openalex.org/W4250955649","https://openalex.org/W4301258660","https://openalex.org/W6634752728"],"related_works":[],"abstract_inverted_index":{"We":[0],"study":[1],"a":[2,33,37,81,98,115,171,177,182],"new":[3],"class":[4],"of":[5,20,28,30,32,124,139,158,163,176],"codes":[6,108],"for":[7,58,94],"lossy":[8],"compression":[9],"with":[10,89,130],"the":[11,17,85,90,112,152,159,164,186],"squared-error":[12],"distortion":[13,132,149],"criterion,":[14],"designed":[15,117],"using":[16],"statistical":[18],"framework":[19],"high-dimensional":[21],"linear":[22,26],"regression.":[23],"Codewords":[24],"are":[25,109],"combinations":[27],"subsets":[29],"columns":[31],"design":[34],"matrix.":[35],"Called":[36],"sparse":[38,41,106,153],"superposition":[39],"or":[40],"regression":[42,107,154],"codebook,":[43],"this":[44],"structure":[45],"is":[46,102,181],"motivated":[47],"by":[48,54],"an":[49,61,120],"analogous":[50],"construction":[51],"proposed":[52],"recently":[53],"Barron":[55],"and":[56,74],"Joseph":[57],"communication":[59],"over":[60],"Additive":[62],"White":[63],"Gaussian":[64,72,122,167],"Noise":[65],"channel.":[66],"For":[67],"independent":[68],"identically":[69],"distributed":[70],"(i.i.d)":[71],"sources":[73],"minimum-distance":[75],"encoding,":[76],"we":[77],"show":[78],"that":[79,105],"such":[80],"code":[82],"can":[83,134],"attain":[84],"Shannon":[86],"rate-distortion":[87],"function":[88],"optimal":[91],"error":[92],"exponent,":[93],"all":[95],"distortions":[96],"below":[97],"specified":[99],"value.":[100],"It":[101],"also":[103],"shown":[104],"robust":[110],"in":[111,174,185],"following":[113],"sense:":[114],"codebook":[116],"to":[118,147],"compress":[119,135],"i.i.d":[121,165],"source":[123,138],"variance":[125,140],"\u03c3":[126,143],"<sup":[127,144],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[128,145],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2</sup>":[129,146],"(squared-error)":[131],"D":[133],"any":[136],"ergodic":[137],"less":[141],"than":[142],"within":[148],"D.":[150],"Thus,":[151],"ensemble":[155],"retains":[156],"many":[157],"good":[160],"covering":[161],"properties":[162],"random":[166],"ensemble,":[168],"while":[169],"having":[170],"compact":[172],"representation":[173],"terms":[175],"matrix":[178],"whose":[179],"size":[180],"low-order":[183],"polynomial":[184],"block-length.":[187]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-04T07:58:01.006859","created_date":"2016-06-24T00:00:00"}