{"id":"https://openalex.org/W2104225469","doi":"https://doi.org/10.1109/apccas.2008.4746143","title":"Non-negative matrix factorization for images with Laplacian noise","display_name":"Non-negative matrix factorization for images with Laplacian noise","publication_year":2008,"publication_date":"2008-11-01","ids":{"openalex":"https://openalex.org/W2104225469","doi":"https://doi.org/10.1109/apccas.2008.4746143","mag":"2104225469"},"language":"en","primary_location":{"id":"doi:10.1109/apccas.2008.4746143","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apccas.2008.4746143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems","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/A5008832723","display_name":"Edmund Y. Lam","orcid":"https://orcid.org/0000-0001-6268-950X"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":true,"raw_author_name":"Edmund Y. Lam","raw_affiliation_strings":["Imaging Systems Laboratory, Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China","[Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong]"],"affiliations":[{"raw_affiliation_string":"Imaging Systems Laboratory, Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I889458895"]},{"raw_affiliation_string":"[Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong]","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5008832723"],"corresponding_institution_ids":["https://openalex.org/I889458895"],"apc_list":null,"apc_paid":null,"fwci":0.3798,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.62605863,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"798","last_page":"801"},"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":0.9927999973297119,"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/T10320","display_name":"Neural Networks and Applications","score":0.9758999943733215,"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/noise","display_name":"Noise (video)","score":0.5550885796546936},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5026516914367676},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.49160438776016235},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.48935288190841675},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.4869759976863861},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.48376965522766113},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.4763762652873993},{"id":"https://openalex.org/keywords/linear-programming","display_name":"Linear programming","score":0.46294084191322327},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.45078882575035095},{"id":"https://openalex.org/keywords/kurtosis","display_name":"Kurtosis","score":0.4467419683933258},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.44325655698776245},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4423609972000122},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.43740198016166687},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4342879056930542},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4075120687484741},{"id":"https://openalex.org/keywords/laplace-operator","display_name":"Laplace operator","score":0.3701384663581848},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26609647274017334},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1832142472267151},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.14620277285575867},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.1325710415840149}],"concepts":[{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5550885796546936},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5026516914367676},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.49160438776016235},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48935288190841675},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.4869759976863861},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.48376965522766113},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.4763762652873993},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.46294084191322327},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.45078882575035095},{"id":"https://openalex.org/C166963901","wikidata":"https://www.wikidata.org/wiki/Q287251","display_name":"Kurtosis","level":2,"score":0.4467419683933258},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.44325655698776245},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4423609972000122},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.43740198016166687},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4342879056930542},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4075120687484741},{"id":"https://openalex.org/C165700671","wikidata":"https://www.wikidata.org/wiki/Q203484","display_name":"Laplace operator","level":2,"score":0.3701384663581848},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26609647274017334},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1832142472267151},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.14620277285575867},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.1325710415840149},{"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/apccas.2008.4746143","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apccas.2008.4746143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.332.449","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.332.449","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.eee.hku.hk/optima/pub/conference/0811_APCCASb.pdf","raw_type":"text"},{"id":"pmh:oai:hub.hku.hk:10722/62139","is_oa":false,"landing_page_url":"http://hdl.handle.net/10722/62139","pdf_url":null,"source":{"id":"https://openalex.org/S4377196271","display_name":"The HKU Scholars Hub (University of Hong Kong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I889458895","host_organization_name":"University of Hong Kong","host_organization_lineage":["https://openalex.org/I889458895"],"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_Paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1489793438","https://openalex.org/W1514159701","https://openalex.org/W1902027874","https://openalex.org/W1981745143","https://openalex.org/W2049633694","https://openalex.org/W2061310904","https://openalex.org/W2065506507","https://openalex.org/W2068339764","https://openalex.org/W2099595379","https://openalex.org/W2108153296","https://openalex.org/W2116216716","https://openalex.org/W2118718620","https://openalex.org/W2127295759","https://openalex.org/W2296319761","https://openalex.org/W2308181013","https://openalex.org/W4250589301","https://openalex.org/W6677759377"],"related_works":["https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2972997031","https://openalex.org/W34555840","https://openalex.org/W2146544734"],"abstract_inverted_index":{"This":[0,16],"paper":[1],"is":[2,31,85,93],"concerned":[3],"with":[4],"the":[5,21,43,46,50,56,70,110,118],"design":[6],"of":[7,23,35],"a":[8,32,36],"non-negative":[9],"matrix":[10],"factorization":[11],"algorithm":[12],"for":[13,45,88,95,109],"image":[14,30],"analysis.":[15],"can":[17],"be":[18,82],"used":[19],"in":[20],"context":[22],"blind":[24],"source":[25],"separation,":[26],"where":[27],"each":[28],"observed":[29,57],"linear":[33,47,131],"combination":[34,48],"few":[37],"basis":[38],"functions,":[39],"and":[40,49,102,127],"that":[41],"both":[42],"coefficients":[44],"bases":[51],"are":[52,59,105],"unknown.":[53],"In":[54],"addition,":[55],"images":[58],"commonly":[60],"corrupted":[61],"by":[62],"noise.":[63],"While":[64],"algorithms":[65],"have":[66],"been":[67],"developed":[68],"when":[69,103],"noise":[71,107],"obeys":[72],"Gaussian":[73],"or":[74],"Poisson":[75],"statistics,":[76],"here":[77],"we":[78],"take":[79],"it":[80,129],"to":[81,114],"Laplacian,":[83],"which":[84],"more":[86],"representative":[87],"other":[89],"leptokurtic":[90],"distributions.":[91],"It":[92],"applicable":[94],"cases":[96],"such":[97],"as":[98,120],"transform":[99],"coefficient":[100],"distributions":[101],"there":[104],"insufficient":[106],"sources":[108],"central":[111],"limit":[112],"theorem":[113],"apply.":[115],"We":[116],"formulate":[117],"problem":[119],"an":[121],"L":[122],"<sub":[123],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[124],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[125],"minimization":[126],"solve":[128],"via":[130],"programming.":[132]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":4},{"year":2017,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}