{"id":"https://openalex.org/W2403526853","doi":"https://doi.org/10.1109/mlsp.2016.7738830","title":"Towards optimal nonlinearities for sparse recovery using higher-order statistics","display_name":"Towards optimal nonlinearities for sparse recovery using higher-order statistics","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2403526853","doi":"https://doi.org/10.1109/mlsp.2016.7738830","mag":"2403526853"},"language":"en","primary_location":{"id":"doi:10.1109/mlsp.2016.7738830","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2016.7738830","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":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1605.08201","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102872941","display_name":"Steffen Limmer","orcid":"https://orcid.org/0000-0003-4287-7129"},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Steffen Limmer","raw_affiliation_strings":["Network Information Theory Group, Technische Universit\u00e4t, Berlin"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Network Information Theory Group, Technische Universit\u00e4t, Berlin","institution_ids":["https://openalex.org/I4577782"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068274314","display_name":"S\u0142awomir Sta\u0144czak","orcid":"https://orcid.org/0000-0003-3829-4668"},"institutions":[{"id":"https://openalex.org/I2800274787","display_name":"Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute","ror":"https://ror.org/02tbr6331","country_code":"DE","type":"facility","lineage":["https://openalex.org/I2800274787","https://openalex.org/I4923324"]},{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Slawomir Stanczak","raw_affiliation_strings":["Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Berlin, Germany","Network Information Theory Group, Technische Universit\u00e4t, Berlin"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Berlin, Germany","institution_ids":["https://openalex.org/I2800274787"]},{"raw_affiliation_string":"Network Information Theory Group, Technische Universit\u00e4t, Berlin","institution_ids":["https://openalex.org/I4577782"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2199,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.32629108,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":1.0,"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/T11447","display_name":"Blind Source Separation Techniques","score":1.0,"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/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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9983999729156494,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.59837406873703},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5044771432876587},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.49125784635543823},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4761476516723633},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.46095824241638184},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.4565718472003937},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.44516417384147644},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41913264989852905},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.4114036560058594},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1070687472820282}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.59837406873703},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5044771432876587},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.49125784635543823},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4761476516723633},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46095824241638184},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4565718472003937},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.44516417384147644},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41913264989852905},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.4114036560058594},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1070687472820282},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1109/mlsp.2016.7738830","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mlsp.2016.7738830","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:arXiv.org:1605.08201","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1605.08201","pdf_url":"https://arxiv.org/pdf/1605.08201","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,"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":"mag:2403526853","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/1605.08201","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,"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":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:publica.fraunhofer.de:publica/397202","is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/397202","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"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":"conference paper"},{"id":"doi:10.48550/arxiv.1605.08201","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1605.08201","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,"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":null,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.17023/9vwg-4p76","is_oa":true,"landing_page_url":"https://doi.org/10.17023/9vwg-4p76","pdf_url":null,"source":{"id":"https://openalex.org/S7407051697","display_name":"IEEE RESOURCE CENTERS","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Audiovisual"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1605.08201","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1605.08201","pdf_url":"https://arxiv.org/pdf/1605.08201","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,"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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2403526853.pdf","grobid_xml":"https://content.openalex.org/works/W2403526853.grobid-xml"},"referenced_works_count":18,"referenced_works":["https://openalex.org/W83428004","https://openalex.org/W606515514","https://openalex.org/W1493645081","https://openalex.org/W1965392255","https://openalex.org/W1972194652","https://openalex.org/W1989574703","https://openalex.org/W2020390700","https://openalex.org/W2060776278","https://openalex.org/W2082639035","https://openalex.org/W2100556411","https://openalex.org/W2111655426","https://openalex.org/W2128405968","https://openalex.org/W2326587871","https://openalex.org/W2953067105","https://openalex.org/W3100774664","https://openalex.org/W3103737847","https://openalex.org/W6638355909","https://openalex.org/W6785434368"],"related_works":["https://openalex.org/W2950786697","https://openalex.org/W3097232617","https://openalex.org/W2626540006","https://openalex.org/W3133264924","https://openalex.org/W2793425295","https://openalex.org/W3208923735","https://openalex.org/W2950038553","https://openalex.org/W2770362671","https://openalex.org/W2950771634","https://openalex.org/W2111200589","https://openalex.org/W2554489331","https://openalex.org/W1548385407","https://openalex.org/W1414340231","https://openalex.org/W3209502942","https://openalex.org/W2921972419","https://openalex.org/W3080806838","https://openalex.org/W2156781223","https://openalex.org/W2183252920","https://openalex.org/W2799985592","https://openalex.org/W3210624872"],"abstract_inverted_index":{"We":[0],"consider":[1],"machine":[2],"learning":[3],"techniques":[4],"to":[5,23,85,112,120,126,134],"develop":[6],"low-latency":[7],"approximate":[8],"solutions":[9],"for":[10,46,160],"a":[11,20,52,57,62,67,121,141],"class":[12],"of":[13,26,34,49,61,70,78,87,103,143],"inverse":[14],"problems.":[15],"More":[16],"precisely,":[17],"we":[18,40,139],"use":[19],"probabilistic":[21],"approach":[22],"the":[24,35,42,76,79,92,153],"problem":[25],"recovering":[27],"sparse":[28],"stochastic":[29],"signals":[30],"that":[31,86,150],"are":[32,170],"members":[33],"lp-balls.":[36],"In":[37],"this":[38],"context,":[39],"analyze":[41],"Bayesian":[43],"mean-square-error":[44],"(MSE)":[45],"two":[47],"types":[48],"estimators:":[50],"(i)":[51],"linear":[53,63,89,145],"estimator":[54,59,82],"and":[55,115,147,166],"(ii)":[56],"structured":[58],"composed":[60],"operator":[64],"followed":[65],"by":[66,101],"Cartesian":[68],"product":[69],"univariate":[71],"nonlinear":[72,81,93,148],"mappings.":[73],"By":[74,132],"construction,":[75],"complexity":[77],"proposed":[80,108],"is":[83,158],"comparable":[84],"its":[88],"counterpart":[90],"since":[91],"mapping":[94],"can":[95],"be":[96],"implemented":[97],"efficiently":[98],"in":[99,152],"hardware":[100,128],"means":[102],"look-up":[104],"tables":[105],"(LUTs).":[106],"The":[107,156],"structure":[109],"lends":[110],"itself":[111],"neural":[113],"networks":[114],"iterative":[116,165],"shrinkage/thresholding-type":[117],"algorithms":[118],"restricted":[119],"single":[122],"iteration":[123],"(e.g.":[124],"due":[125],"imposed":[127],"or":[129],"latency":[130],"constraints).":[131],"resorting":[133],"an":[135],"alternating":[136],"minimization":[137],"technique,":[138],"obtain":[140],"sequence":[142],"optimized":[144],"operators":[146],"mappings":[149],"converge":[151],"MSE":[154],"objective.":[155],"result":[157],"attractive":[159],"real-time":[161],"applications":[162],"where":[163],"general":[164],"convex":[167],"optimization":[168],"methods":[169],"infeasible.":[171]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2022-10-05T00:00:00"}
