{"id":"https://openalex.org/W2920296973","doi":"https://doi.org/10.1109/acssc.2018.8645419","title":"Spread and Sparse: Learning Interpretable Transforms for Bandlimited Signals on Directed Graphs","display_name":"Spread and Sparse: Learning Interpretable Transforms for Bandlimited Signals on Directed Graphs","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2920296973","doi":"https://doi.org/10.1109/acssc.2018.8645419","mag":"2920296973"},"language":"en","primary_location":{"id":"doi:10.1109/acssc.2018.8645419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2018.8645419","pdf_url":null,"source":{"id":"https://openalex.org/S4363608623","display_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","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/A5062141963","display_name":"Rasoul Shafipour","orcid":"https://orcid.org/0000-0002-2996-2671"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Rasoul Shafipour","raw_affiliation_strings":["Dept. of ECE, University of Rochester"],"affiliations":[{"raw_affiliation_string":"Dept. of ECE, University of Rochester","institution_ids":["https://openalex.org/I5388228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006078163","display_name":"Gonzalo Mateos","orcid":"https://orcid.org/0000-0002-9847-6298"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gonzalo Mateos","raw_affiliation_strings":["Dept. of ECE, University of Rochester"],"affiliations":[{"raw_affiliation_string":"Dept. of ECE, University of Rochester","institution_ids":["https://openalex.org/I5388228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5062141963"],"corresponding_institution_ids":["https://openalex.org/I5388228"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22616186,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"742","last_page":"746"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9991000294685364,"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":0.9991000294685364,"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":0.9793999791145325,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bandlimiting","display_name":"Bandlimiting","score":0.8405770659446716},{"id":"https://openalex.org/keywords/orthonormal-basis","display_name":"Orthonormal basis","score":0.6940100789070129},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5232433676719666},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.5096485614776611},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5018496513366699},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.48756664991378784},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48641014099121094},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4525423049926758},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.4418479800224304},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4326525032520294},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4170447289943695},{"id":"https://openalex.org/keywords/discrete-frequency-domain","display_name":"Discrete frequency domain","score":0.4150785207748413},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.381645530462265},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3793220520019531},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3156290650367737},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.10055491328239441}],"concepts":[{"id":"https://openalex.org/C129997835","wikidata":"https://www.wikidata.org/wiki/Q806263","display_name":"Bandlimiting","level":3,"score":0.8405770659446716},{"id":"https://openalex.org/C5806529","wikidata":"https://www.wikidata.org/wiki/Q2365325","display_name":"Orthonormal basis","level":2,"score":0.6940100789070129},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5232433676719666},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.5096485614776611},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5018496513366699},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.48756664991378784},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48641014099121094},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4525423049926758},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.4418479800224304},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4326525032520294},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4170447289943695},{"id":"https://openalex.org/C23548689","wikidata":"https://www.wikidata.org/wiki/Q5282049","display_name":"Discrete frequency domain","level":3,"score":0.4150785207748413},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.381645530462265},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3793220520019531},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3156290650367737},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.10055491328239441},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acssc.2018.8645419","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2018.8645419","pdf_url":null,"source":{"id":"https://openalex.org/S4363608623","display_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 52nd Asilomar Conference on Signals, Systems, and Computers","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":17,"referenced_works":["https://openalex.org/W1698699930","https://openalex.org/W2086953401","https://openalex.org/W2101491865","https://openalex.org/W2148578298","https://openalex.org/W2160547390","https://openalex.org/W2160660350","https://openalex.org/W2163398148","https://openalex.org/W2182708456","https://openalex.org/W2259160646","https://openalex.org/W2573721906","https://openalex.org/W2611356371","https://openalex.org/W2794319422","https://openalex.org/W2795527331","https://openalex.org/W2796431263","https://openalex.org/W2963813647","https://openalex.org/W4301014524","https://openalex.org/W6749986051"],"related_works":["https://openalex.org/W2953058328","https://openalex.org/W1542224353","https://openalex.org/W2370850565","https://openalex.org/W1661087619","https://openalex.org/W2111364039","https://openalex.org/W1828802557","https://openalex.org/W1642462315","https://openalex.org/W4205233061","https://openalex.org/W2013721961","https://openalex.org/W2019864311"],"abstract_inverted_index":{"We":[0],"address":[1],"the":[2,20,28,46,70,81,100,105,110,113,124,129,132],"problem":[3],"of":[4,22,40,54,84,109,119,131],"learning":[5,26],"a":[6,60,65,76,96],"sparsifying":[7],"graph":[8],"Fourier":[9,23],"transform":[10],"(GFT)":[11],"for":[12],"compressible":[13],"signals":[14,111],"on":[15,80],"directed":[16],"graphs":[17],"(digraphs).":[18],"Blending":[19],"merits":[21,130],"and":[24,50,103],"dictionary":[25,62],"representations,":[27],"goal":[29],"is":[30,90],"to":[31,45],"obtain":[32],"an":[33],"orthonormal":[34],"basis":[35],"that":[36],"captures":[37],"spread":[38],"modes":[39],"signal":[41],"variation":[42],"with":[43,75],"respect":[44],"underlying":[47],"network":[48],"topology,":[49],"yields":[51],"parsimonious":[52],"representations":[53,108],"bandlimited":[55],"signals.":[56,86],"Accordingly,":[57],"we":[58],"learn":[59],"data-adapted":[61],"by":[63],"minimizing":[64,95],"spectral":[66],"dispersion":[67],"criterion":[68],"over":[69,99],"achievable":[71],"frequency":[72,117],"range,":[73],"along":[74],"sparsity-promoting":[77],"regularization":[78],"term":[79],"GFT":[82,134],"coefficients":[83],"training":[85,114],"An":[87],"iterative":[88],"algorithm":[89],"developed":[91],"which":[92],"alternates":[93],"between":[94],"smooth":[97],"objective":[98],"Stiefel":[101],"manifold,":[102],"soft-thresholding":[104],"graph-spectral":[106],"domain":[107],"in":[112],"set.":[115],"A":[116],"analysis":[118],"temperature":[120],"measurements":[121],"recorded":[122],"across":[123],"contiguous":[125],"United":[126],"States":[127],"illustrates":[128],"novel":[133],"design.":[135]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
