{"id":"https://openalex.org/W2938493800","doi":"https://doi.org/10.1109/icassp.2019.8682769","title":"Learning Laplacian Matrix from Bandlimited Graph Signals","display_name":"Learning Laplacian Matrix from Bandlimited Graph Signals","publication_year":2019,"publication_date":"2019-04-17","ids":{"openalex":"https://openalex.org/W2938493800","doi":"https://doi.org/10.1109/icassp.2019.8682769","mag":"2938493800"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2019.8682769","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682769","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5077247843","display_name":"Batiste Le Bars","orcid":null},"institutions":[{"id":"https://openalex.org/I11559806","display_name":"\u00c9cole Normale Sup\u00e9rieure Paris-Saclay","ror":"https://ror.org/00hx6zz33","country_code":"FR","type":"education","lineage":["https://openalex.org/I11559806","https://openalex.org/I277688954"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Batiste Le Bars","raw_affiliation_strings":["CMLA, ENS Cachan, CNRS, Universit\u00e9 Paris-Saclay, Cachan, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CMLA, ENS Cachan, CNRS, Universit\u00e9 Paris-Saclay, Cachan, France","institution_ids":["https://openalex.org/I11559806","https://openalex.org/I1294671590","https://openalex.org/I277688954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103241935","display_name":"Pierre Humbert","orcid":null},"institutions":[{"id":"https://openalex.org/I11559806","display_name":"\u00c9cole Normale Sup\u00e9rieure Paris-Saclay","ror":"https://ror.org/00hx6zz33","country_code":"FR","type":"education","lineage":["https://openalex.org/I11559806","https://openalex.org/I277688954"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Pierre Humbert","raw_affiliation_strings":["CMLA, ENS Cachan, CNRS, Universit\u00e9 Paris-Saclay, Cachan, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CMLA, ENS Cachan, CNRS, Universit\u00e9 Paris-Saclay, Cachan, France","institution_ids":["https://openalex.org/I11559806","https://openalex.org/I1294671590","https://openalex.org/I277688954"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090929380","display_name":"Laurent Oudre","orcid":"https://orcid.org/0000-0002-4750-2265"},"institutions":[{"id":"https://openalex.org/I4210091279","display_name":"Universit\u00e9 Sorbonne Paris Nord","ror":"https://ror.org/0199hds37","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210091279"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Laurent Oudre","raw_affiliation_strings":["L2TI, Universit\u00e9 Paris 13, Villetaneuse, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"L2TI, Universit\u00e9 Paris 13, Villetaneuse, France","institution_ids":["https://openalex.org/I4210091279"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036605458","display_name":"Argyris Kalogeratos","orcid":"https://orcid.org/0000-0003-1436-3593"},"institutions":[{"id":"https://openalex.org/I11559806","display_name":"\u00c9cole Normale Sup\u00e9rieure Paris-Saclay","ror":"https://ror.org/00hx6zz33","country_code":"FR","type":"education","lineage":["https://openalex.org/I11559806","https://openalex.org/I277688954"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I277688954","display_name":"Universit\u00e9 Paris-Saclay","ror":"https://ror.org/03xjwb503","country_code":"FR","type":"education","lineage":["https://openalex.org/I277688954"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Argyris Kalogeratos","raw_affiliation_strings":["CMLA, ENS Cachan, CNRS, Universit\u00e9 Paris-Saclay, Cachan, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CMLA, ENS Cachan, CNRS, Universit\u00e9 Paris-Saclay, Cachan, France","institution_ids":["https://openalex.org/I11559806","https://openalex.org/I1294671590","https://openalex.org/I277688954"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6401,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.88330981,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2937","last_page":"2941"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10064","display_name":"Complex Network Analysis Techniques","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9678000211715698,"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/laplacian-matrix","display_name":"Laplacian matrix","score":0.8086404800415039},{"id":"https://openalex.org/keywords/bandlimiting","display_name":"Bandlimiting","score":0.76973557472229},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5496457815170288},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5433971285820007},{"id":"https://openalex.org/keywords/spectral-graph-theory","display_name":"Spectral graph theory","score":0.5088937282562256},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5005033016204834},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.46357420086860657},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.45503872632980347},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.45037874579429626},{"id":"https://openalex.org/keywords/eigendecomposition-of-a-matrix","display_name":"Eigendecomposition of a matrix","score":0.4301106631755829},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3716384768486023},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3478226065635681},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32584142684936523},{"id":"https://openalex.org/keywords/voltage-graph","display_name":"Voltage graph","score":0.3151690363883972},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.17782631516456604},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.12531059980392456}],"concepts":[{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.8086404800415039},{"id":"https://openalex.org/C129997835","wikidata":"https://www.wikidata.org/wiki/Q806263","display_name":"Bandlimiting","level":3,"score":0.76973557472229},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5496457815170288},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5433971285820007},{"id":"https://openalex.org/C74003402","wikidata":"https://www.wikidata.org/wiki/Q3180727","display_name":"Spectral graph theory","level":5,"score":0.5088937282562256},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5005033016204834},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.46357420086860657},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45503872632980347},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.45037874579429626},{"id":"https://openalex.org/C169756996","wikidata":"https://www.wikidata.org/wiki/Q194919","display_name":"Eigendecomposition of a matrix","level":3,"score":0.4301106631755829},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3716384768486023},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3478226065635681},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32584142684936523},{"id":"https://openalex.org/C22149727","wikidata":"https://www.wikidata.org/wiki/Q7940747","display_name":"Voltage graph","level":4,"score":0.3151690363883972},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.17782631516456604},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.12531059980392456},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2019.8682769","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2019.8682769","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1511986666","https://openalex.org/W1537592944","https://openalex.org/W1540550726","https://openalex.org/W1690143098","https://openalex.org/W1849282082","https://openalex.org/W1975900269","https://openalex.org/W1991252559","https://openalex.org/W1992179289","https://openalex.org/W2000769684","https://openalex.org/W2022798427","https://openalex.org/W2030319053","https://openalex.org/W2055588122","https://openalex.org/W2071389326","https://openalex.org/W2101234009","https://openalex.org/W2101491865","https://openalex.org/W2119699244","https://openalex.org/W2132555912","https://openalex.org/W2138019504","https://openalex.org/W2142880609","https://openalex.org/W2151128232","https://openalex.org/W2161305109","https://openalex.org/W2161763921","https://openalex.org/W2164900957","https://openalex.org/W2165874743","https://openalex.org/W2274083067","https://openalex.org/W2553125747","https://openalex.org/W2610919036","https://openalex.org/W2959406683","https://openalex.org/W2963384510","https://openalex.org/W2963702033","https://openalex.org/W2964012239","https://openalex.org/W2964171990","https://openalex.org/W2964214281","https://openalex.org/W2964228184","https://openalex.org/W3105775869","https://openalex.org/W4230938240","https://openalex.org/W4297689879","https://openalex.org/W6681235718","https://openalex.org/W6682227116","https://openalex.org/W6684199539","https://openalex.org/W6684578312","https://openalex.org/W6689213722","https://openalex.org/W6694492338","https://openalex.org/W6730195713","https://openalex.org/W6734267277","https://openalex.org/W6766505652","https://openalex.org/W6786560606"],"related_works":["https://openalex.org/W2188097034","https://openalex.org/W3004345458","https://openalex.org/W2478475484","https://openalex.org/W2029616478","https://openalex.org/W2081084405","https://openalex.org/W4386468260","https://openalex.org/W2795850273","https://openalex.org/W2537944959","https://openalex.org/W1002309053","https://openalex.org/W4318995835"],"abstract_inverted_index":{"In":[0],"this":[1,120],"paper,":[2],"we":[3],"present":[4],"a":[5,104,123],"method":[6,24,126],"for":[7],"learning":[8,40,140],"an":[9],"underlying":[10],"graph":[11,15,39,57,139,146],"topology":[12],"using":[13],"observed":[14],"signals":[16,51,72],"as":[17,35],"training":[18],"data.":[19],"The":[20,86,109],"novelty":[21],"of":[22,29,92,97],"our":[23],"lies":[25],"on":[26,131,142],"the":[27,38,43,48,68,79,83,90,93,98,115,132,138],"combination":[28],"two":[30],"assumptions":[31],"that":[32,50,71,114,127],"are":[33,52,73],"imposed":[34],"constraints":[36],"to":[37,56,103,118],"process:":[41],"i)":[42],"standard":[44],"assumption":[45,70,88],"used":[46],"in":[47,78,82,137],"literature":[49],"smooth":[53],"with":[54,66],"respect":[55],"structure":[58],"(i.e.":[59],"small":[60],"signal":[61],"variation":[62],"at":[63],"adjacent":[64],"nodes),":[65],"ii)":[67],"additional":[69],"bandlimited,":[74],"which":[75],"implies":[76],"sparsity":[77],"signals'":[80],"representation":[81],"spectral":[84],"domain.":[85],"latter":[87],"affects":[89],"inference":[91],"whole":[94],"eigenvalue":[95],"decomposition":[96],"Laplacian":[99],"matrix":[100],"and":[101,144],"leads":[102],"challenging":[105],"new":[106],"optimization":[107],"problem.":[108],"conducted":[110],"experimental":[111],"evaluation":[112],"shows":[113],"proposed":[116],"algorithm":[117],"solve":[119],"problem":[121],"outperforms":[122],"reference":[124],"state-of-the-art":[125],"is":[128],"based":[129],"only":[130],"smoothness":[133],"assumption,":[134],"when":[135],"compared":[136],"task":[141],"synthetic":[143],"real":[145],"signals.":[147]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}