{"id":"https://openalex.org/W2159839892","doi":"https://doi.org/10.1109/ecctd.2009.5275015","title":"A cooperation index based on correlation matrix spectrum and r\u00e1enyi entropy","display_name":"A cooperation index based on correlation matrix spectrum and r\u00e1enyi entropy","publication_year":2009,"publication_date":"2009-08-01","ids":{"openalex":"https://openalex.org/W2159839892","doi":"https://doi.org/10.1109/ecctd.2009.5275015","mag":"2159839892"},"language":"en","primary_location":{"id":"doi:10.1109/ecctd.2009.5275015","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ecctd.2009.5275015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 European Conference on Circuit Theory and Design","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/A5010402850","display_name":"Marco Righero","orcid":"https://orcid.org/0000-0002-4652-8041"},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Polytechnic University of Turin","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Marco Righero","raw_affiliation_strings":["Electronic Engineering Department, Politecnico di Turino, Torino, Italy"],"affiliations":[{"raw_affiliation_string":"Electronic Engineering Department, Politecnico di Turino, Torino, Italy","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113673122","display_name":"O. De Feo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210167176","display_name":"R\u00fctter Soceco (Switzerland)","ror":"https://ror.org/05n6yb463","country_code":"CH","type":"company","lineage":["https://openalex.org/I4210167176"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Oscar De Feo","raw_affiliation_strings":["Solianis Monitoring AG, Zurich, Switzerland"],"affiliations":[{"raw_affiliation_string":"Solianis Monitoring AG, Zurich, Switzerland","institution_ids":["https://openalex.org/I4210167176"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038007353","display_name":"Mario Biey","orcid":null},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Polytechnic University of Turin","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Mario Biey","raw_affiliation_strings":["Electronic Engineering Department, Politecnico di Turino, Torino, Italy"],"affiliations":[{"raw_affiliation_string":"Electronic Engineering Department, Politecnico di Turino, Torino, Italy","institution_ids":["https://openalex.org/I177477856"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010402850"],"corresponding_institution_ids":["https://openalex.org/I177477856"],"apc_list":null,"apc_paid":null,"fwci":2.3532,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.91818366,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"25","issue":null,"first_page":"466","last_page":"469"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9785000085830688,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9459999799728394,"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/T10581","display_name":"Neural dynamics and brain function","score":0.9391999840736389,"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/principal-component-analysis","display_name":"Principal component analysis","score":0.8057853579521179},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.62087082862854},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5249452590942383},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5144546031951904},{"id":"https://openalex.org/keywords/notice","display_name":"Notice","score":0.5067303776741028},{"id":"https://openalex.org/keywords/generalized-entropy-index","display_name":"Generalized entropy index","score":0.49331918358802795},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.4881335198879242},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.4691658914089203},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.44358229637145996},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4265492558479309},{"id":"https://openalex.org/keywords/r\u00e9nyi-entropy","display_name":"R\u00e9nyi entropy","score":0.4208550453186035},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4145321846008301},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40296101570129395},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.37967777252197266},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33471643924713135},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2609296441078186},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.11868861317634583},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09046480059623718},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.08716100454330444}],"concepts":[{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.8057853579521179},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.62087082862854},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5249452590942383},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5144546031951904},{"id":"https://openalex.org/C2779913896","wikidata":"https://www.wikidata.org/wiki/Q7063001","display_name":"Notice","level":2,"score":0.5067303776741028},{"id":"https://openalex.org/C166832808","wikidata":"https://www.wikidata.org/wiki/Q254487","display_name":"Generalized entropy index","level":3,"score":0.49331918358802795},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.4881335198879242},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.4691658914089203},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.44358229637145996},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4265492558479309},{"id":"https://openalex.org/C142611142","wikidata":"https://www.wikidata.org/wiki/Q1433083","display_name":"R\u00e9nyi entropy","level":3,"score":0.4208550453186035},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4145321846008301},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40296101570129395},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37967777252197266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33471643924713135},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2609296441078186},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.11868861317634583},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09046480059623718},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.08716100454330444},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","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/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ecctd.2009.5275015","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ecctd.2009.5275015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 European Conference on Circuit Theory and Design","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1549386224","https://openalex.org/W1561795226","https://openalex.org/W1967008192","https://openalex.org/W1979957032","https://openalex.org/W2000990910","https://openalex.org/W2020853694","https://openalex.org/W2027607571","https://openalex.org/W2092939357","https://openalex.org/W2141394518","https://openalex.org/W2148694408","https://openalex.org/W2478708596","https://openalex.org/W2616851730","https://openalex.org/W3124661333","https://openalex.org/W3144154667","https://openalex.org/W3176826459","https://openalex.org/W4250857377","https://openalex.org/W4299429830"],"related_works":["https://openalex.org/W1975632186","https://openalex.org/W3027745756","https://openalex.org/W4297802450","https://openalex.org/W2087834705","https://openalex.org/W50310712","https://openalex.org/W2106194785","https://openalex.org/W4391309435","https://openalex.org/W2338872609","https://openalex.org/W2963521009","https://openalex.org/W2104360501"],"abstract_inverted_index":{"Indices":[0],"based":[1,37,90],"on":[2,38,91],"correlation":[3],"or":[4,20],"more":[5],"subtle":[6],"strategies":[7],"are":[8,57,68,127],"among":[9],"the":[10,29,59,65,80,125,140],"standard":[11],"ways":[12],"to":[13,108,120,138],"infer":[14],"dependencies":[15],"(i.e.,":[16],"exchange":[17],"of":[18,24,50],"information":[19],"coupling)":[21],"in":[22,28],"aggregations":[23],"different":[25],"systems":[26],"observed":[27],"time":[30],"domain.":[31],"We":[32],"propose":[33],"a":[34,131],"new":[35],"index":[36,75],"Renyi":[39],"entropy":[40],"and":[41,98,130],"confront":[42],"it":[43],"with":[44,94,100],"other":[45,81],"indices,":[46],"studying":[47],"if":[48],"some":[49],"these":[51],"techniques":[52],"can":[53],"recognize":[54],"when":[55,64,124],"we":[56,85],"observing":[58],"same":[60],"system":[61],"twice,":[62],"even":[63],"observation":[66],"conditions":[67],"bad.":[69],"It":[70],"turns":[71],"out":[72],"that":[73,87],"our":[74],"gives":[76],"better":[77],"results":[78],"than":[79],"examined":[82],"ones.":[83],"Moreover,":[84],"notice":[86],"those":[88],"indices":[89],"data":[92,126],"processed":[93],"state":[95,112],"space":[96,113],"reconstruction":[97,114],"filtered":[99],"principal":[101,132],"component":[102,133],"analysis":[103,134],"are,":[104],"generally,":[105],"less":[106],"sensitive":[107],"bad":[109],"observations.":[110],"However,":[111],"by":[115],"itself":[116],"is":[117,136],"not":[118],"enough":[119],"obtain":[121],"good":[122],"performances":[123],"very":[128],"noisy,":[129],"filter":[135],"needed":[137],"improve":[139],"results.":[141]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2013,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
