{"id":"https://openalex.org/W2514305317","doi":"https://doi.org/10.1109/icip.2016.7532777","title":"Learning dictionaries from correlated data: Application to fMRI data analysis","display_name":"Learning dictionaries from correlated data: Application to fMRI data analysis","publication_year":2016,"publication_date":"2016-08-17","ids":{"openalex":"https://openalex.org/W2514305317","doi":"https://doi.org/10.1109/icip.2016.7532777","mag":"2514305317"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7532777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532777","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","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/A5084681382","display_name":"Abd\u2010Krim Seghouane","orcid":"https://orcid.org/0000-0003-4619-734X"},"institutions":[{"id":"https://openalex.org/I165779595","display_name":"The University of Melbourne","ror":"https://ror.org/01ej9dk98","country_code":"AU","type":"education","lineage":["https://openalex.org/I165779595"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Abd-Krim Seghouane","raw_affiliation_strings":["Department of EEE, University of Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of EEE, University of Melbourne, Australia","institution_ids":["https://openalex.org/I165779595"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5007304996","display_name":"Muhammad Usman Khalid","orcid":"https://orcid.org/0000-0003-1636-3351"},"institutions":[{"id":"https://openalex.org/I118347636","display_name":"Australian National University","ror":"https://ror.org/019wvm592","country_code":"AU","type":"education","lineage":["https://openalex.org/I118347636"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Muhammad Usman Khalid","raw_affiliation_strings":["College of Engineering and Computer Science, The Australian National University, Canberra, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Engineering and Computer Science, The Australian National University, Canberra, Australia","institution_ids":["https://openalex.org/I118347636"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5105,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.64690707,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2340","last_page":"2344"},"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":0.9994000196456909,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/functional-magnetic-resonance-imaging","display_name":"Functional magnetic resonance imaging","score":0.7346539497375488},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7076207995414734},{"id":"https://openalex.org/keywords/singular-value-decomposition","display_name":"Singular value decomposition","score":0.6559916138648987},{"id":"https://openalex.org/keywords/matrix-norm","display_name":"Matrix norm","score":0.5794178247451782},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5516713857650757},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.547034740447998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5418331027030945},{"id":"https://openalex.org/keywords/independent-component-analysis","display_name":"Independent component analysis","score":0.5374370813369751},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5235167145729065},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44052308797836304},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.41650599241256714},{"id":"https://openalex.org/keywords/canonical-correlation","display_name":"Canonical correlation","score":0.4100393056869507},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3615829348564148},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.360679030418396},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1939789354801178}],"concepts":[{"id":"https://openalex.org/C2779226451","wikidata":"https://www.wikidata.org/wiki/Q903809","display_name":"Functional magnetic resonance imaging","level":2,"score":0.7346539497375488},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7076207995414734},{"id":"https://openalex.org/C22789450","wikidata":"https://www.wikidata.org/wiki/Q420904","display_name":"Singular value decomposition","level":2,"score":0.6559916138648987},{"id":"https://openalex.org/C92207270","wikidata":"https://www.wikidata.org/wiki/Q939253","display_name":"Matrix norm","level":3,"score":0.5794178247451782},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5516713857650757},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.547034740447998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5418331027030945},{"id":"https://openalex.org/C51432778","wikidata":"https://www.wikidata.org/wiki/Q1259145","display_name":"Independent component analysis","level":2,"score":0.5374370813369751},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5235167145729065},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44052308797836304},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.41650599241256714},{"id":"https://openalex.org/C153874254","wikidata":"https://www.wikidata.org/wiki/Q115542","display_name":"Canonical correlation","level":2,"score":0.4100393056869507},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3615829348564148},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.360679030418396},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1939789354801178},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2016.7532777","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532777","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.8199999928474426}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1528119265","https://openalex.org/W1591116419","https://openalex.org/W1963041479","https://openalex.org/W1979766935","https://openalex.org/W1983407432","https://openalex.org/W1991840148","https://openalex.org/W1995714794","https://openalex.org/W2001235259","https://openalex.org/W2030854293","https://openalex.org/W2061191150","https://openalex.org/W2062195890","https://openalex.org/W2084147096","https://openalex.org/W2103402739","https://openalex.org/W2113107995","https://openalex.org/W2118297240","https://openalex.org/W2129825861","https://openalex.org/W2131556788","https://openalex.org/W2134491286","https://openalex.org/W2147252463","https://openalex.org/W2160547390","https://openalex.org/W3148198191","https://openalex.org/W4235713725","https://openalex.org/W4238149895"],"related_works":["https://openalex.org/W2043573028","https://openalex.org/W2295974754","https://openalex.org/W2782904003","https://openalex.org/W3121932492","https://openalex.org/W2776058083","https://openalex.org/W2118633810","https://openalex.org/W2150953077","https://openalex.org/W4226434912","https://openalex.org/W2002598339","https://openalex.org/W1995410415"],"abstract_inverted_index":{"Sequential":[0],"dictionary":[1,121],"learning":[2],"via":[3],"the":[4,54,76,96,101,106,112,120,127],"K-SVD":[5,55,77],"algorithm":[6,56,78,93,129],"has":[7,49],"been":[8,51],"revealed":[9],"as":[10,20],"a":[11,73,137],"successful":[12],"alternative":[13],"to":[14,68,80],"conventional":[15],"data":[16,31,34,39,61,82,103,140],"driven":[17],"methods":[18],"such":[19],"independent":[21],"component":[22],"analysis":[23,83],"(ICA)":[24],"for":[25,95,115],"functional":[26],"magnetic":[27],"resonance":[28],"imaging":[29],"(fMRI)":[30],"analysis.":[32,62],"fMRI":[33,60,81,102,139],"sets":[35],"are":[36],"however":[37],"structured":[38],"matrices":[40],"with":[41],"notions":[42],"of":[43,75,111,126],"spatio-temporal":[44],"correlation.":[45],"This":[46],"prior":[47,89],"information":[48],"not":[50],"included":[52],"in":[53,59,100,119],"when":[57],"applied":[58],"In":[63],"this":[64,69,88],"paper":[65],"we":[66],"remedy":[67],"situation":[70],"by":[71,84,104],"proposing":[72],"variant":[74],"dedicated":[79],"taking":[85],"into":[86],"account":[87],"information.":[90],"The":[91,124],"proposed":[92,128],"accounts":[94],"known":[97],"correlation":[98],"structure":[99],"using":[105],"squared":[107],"Q,":[108],"R-norm":[109],"instead":[110],"Frobenius":[113],"norm":[114],"rank":[116],"one":[117],"approximation":[118],"update":[122],"stage.":[123],"performance":[125],"is":[130],"illustrated":[131],"through":[132],"simulations":[133],"and":[134],"applications":[135],"on":[136],"real":[138],"set.":[141]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2017,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
