{"id":"https://openalex.org/W2679590114","doi":"https://doi.org/10.1109/icassp.2017.7952698","title":"Disc-GLasso: Discriminative graph learning with sparsity regularization","display_name":"Disc-GLasso: Discriminative graph learning with sparsity regularization","publication_year":2017,"publication_date":"2017-03-01","ids":{"openalex":"https://openalex.org/W2679590114","doi":"https://doi.org/10.1109/icassp.2017.7952698","mag":"2679590114"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2017.7952698","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952698","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5047382767","display_name":"Jiun-Yu Kao","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]},{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jiun-Yu Kao","raw_affiliation_strings":["Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA","Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016854114","display_name":"Dong Tian","orcid":"https://orcid.org/0000-0002-2310-0974"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dong Tian","raw_affiliation_strings":["Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101648073","display_name":"Hassan Mansour","orcid":"https://orcid.org/0000-0002-1667-9885"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hassan Mansour","raw_affiliation_strings":["Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040001106","display_name":"Antonio Ortega","orcid":"https://orcid.org/0000-0001-5403-0940"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Antonio Ortega","raw_affiliation_strings":["Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049841606","display_name":"Anthony Vetro","orcid":"https://orcid.org/0000-0002-8194-573X"},"institutions":[{"id":"https://openalex.org/I4210159266","display_name":"Mitsubishi Electric (United States)","ror":"https://ror.org/053jnhe44","country_code":"US","type":"company","lineage":["https://openalex.org/I1306287861","https://openalex.org/I4210133125","https://openalex.org/I4210159266"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anthony Vetro","raw_affiliation_strings":["Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"Mitsubishi Electric Research Labs (MERL), Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210159266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5047382767"],"corresponding_institution_ids":["https://openalex.org/I1174212","https://openalex.org/I4210159266"],"apc_list":null,"apc_paid":null,"fwci":1.4448,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.8592855,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2956","last_page":"2960"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9921000003814697,"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/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.9814000129699707,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.8338296413421631},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6460235118865967},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5717336535453796},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.554287850856781},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4650633931159973},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4395711421966553},{"id":"https://openalex.org/keywords/dictionary-learning","display_name":"Dictionary learning","score":0.4174809157848358},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3832363784313202},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.23597988486289978},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.09519129991531372}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8338296413421631},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6460235118865967},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5717336535453796},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.554287850856781},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4650633931159973},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4395711421966553},{"id":"https://openalex.org/C2988886741","wikidata":"https://www.wikidata.org/wiki/Q25304494","display_name":"Dictionary learning","level":3,"score":0.4174809157848358},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3832363784313202},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.23597988486289978},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.09519129991531372}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2017.7952698","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952698","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7200000286102295,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1544422618","https://openalex.org/W1990488619","https://openalex.org/W2092420213","https://openalex.org/W2101491865","https://openalex.org/W2105760337","https://openalex.org/W2124543570","https://openalex.org/W2132555912","https://openalex.org/W2160660350","https://openalex.org/W2399508263","https://openalex.org/W2469963130","https://openalex.org/W2626958527","https://openalex.org/W2796728297","https://openalex.org/W6739593606"],"related_works":["https://openalex.org/W1652783584","https://openalex.org/W74886973","https://openalex.org/W2024160000","https://openalex.org/W82679236","https://openalex.org/W2773500201","https://openalex.org/W2104535716","https://openalex.org/W1914651075","https://openalex.org/W1982774199","https://openalex.org/W2510758617","https://openalex.org/W2109693548"],"abstract_inverted_index":{"Learning":[0],"graph":[1,16],"topology":[2],"from":[3],"data":[4,110],"is":[5,65,85],"challenging.":[6],"Previous":[7],"work":[8],"leads":[9],"to":[10,37,74,88,101,125],"learning":[11,32],"graphs":[12,91],"on":[13,108],"which":[14,97],"the":[15,68,75,104,121],"signals":[17,46,53],"used":[18,100],"for":[19,31,92],"training":[20],"are":[21,57,98],"smooth.":[22],"In":[23],"this":[24],"paper,":[25],"we":[26],"propose":[27],"an":[28],"optimization":[29,69],"framework":[30],"multiple":[33],"graphs,":[34,123],"each":[35],"associated":[36],"a":[38,48],"class":[39,49],"of":[40,45,52,95],"signals,":[41,96],"such":[42],"that":[43,112],"representation":[44],"within":[47],"and":[50],"discrimination":[51,119],"in":[54,67,72,127],"different":[55,93,105],"classes":[56],"both":[58],"taken":[59],"into":[60],"consideration.":[61],"A":[62,80],"Fisher-LDA-like":[63],"term":[64],"included":[66],"objective":[70],"function":[71],"addition":[73],"conventional":[76],"Gaussian":[77],"ML":[78],"objective.":[79],"block":[81],"coordinate":[82],"descent":[83],"algorithm":[84],"then":[86,99],"developed":[87],"estimate":[89],"optimal":[90],"categories":[94],"efficiently":[102],"classify":[103],"signals.":[106],"Experiments":[107],"synthetic":[109],"demonstrate":[111],"our":[113],"proposed":[114],"method":[115],"can":[116],"achieve":[117],"better":[118],"between":[120],"learned":[122],"leading":[124],"improvements":[126],"subsequent":[128],"classification":[129],"tasks.":[130]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
