{"id":"https://openalex.org/W3015746494","doi":"https://doi.org/10.1109/icassp40776.2020.9053423","title":"Kernel Ridge Regression with Autocorrelation Prior: Optimal Model and Cross-Validation","display_name":"Kernel Ridge Regression with Autocorrelation Prior: Optimal Model and Cross-Validation","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3015746494","doi":"https://doi.org/10.1109/icassp40776.2020.9053423","mag":"3015746494"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9053423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 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/A5105269463","display_name":"Akira Tanaka","orcid":"https://orcid.org/0000-0003-0055-8234"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Akira Tanaka","raw_affiliation_strings":["Division of Computer Science and Information Technology, Hokkaido University, Sapporo, Japan"],"affiliations":[{"raw_affiliation_string":"Division of Computer Science and Information Technology, Hokkaido University, Sapporo, Japan","institution_ids":["https://openalex.org/I205349734"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077220801","display_name":"Hideyuki Imai","orcid":"https://orcid.org/0000-0002-3793-1146"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hideyuki Imai","raw_affiliation_strings":["Division of Computer Science and Information Technology, Hokkaido University, Sapporo, Japan"],"affiliations":[{"raw_affiliation_string":"Division of Computer Science and Information Technology, Hokkaido University, Sapporo, Japan","institution_ids":["https://openalex.org/I205349734"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5105269463"],"corresponding_institution_ids":["https://openalex.org/I205349734"],"apc_list":null,"apc_paid":null,"fwci":0.3977,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.67322191,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"3872","last_page":"3876"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","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/T12814","display_name":"Gaussian Processes and Bayesian Inference","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/T11236","display_name":"Control Systems and Identification","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9916999936103821,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6614679098129272},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.63640958070755},{"id":"https://openalex.org/keywords/autocorrelation","display_name":"Autocorrelation","score":0.6220093965530396},{"id":"https://openalex.org/keywords/kernel-regression","display_name":"Kernel regression","score":0.5323144793510437},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.528508722782135},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.484681636095047},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.47983667254447937},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.46948227286338806},{"id":"https://openalex.org/keywords/cross-validation","display_name":"Cross-validation","score":0.46929794549942017},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.46386995911598206},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.39688557386398315},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.33633893728256226},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07197168469429016},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.06480059027671814}],"concepts":[{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6614679098129272},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.63640958070755},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.6220093965530396},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.5323144793510437},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.528508722782135},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.484681636095047},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.47983667254447937},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.46948227286338806},{"id":"https://openalex.org/C27181475","wikidata":"https://www.wikidata.org/wiki/Q541014","display_name":"Cross-validation","level":2,"score":0.46929794549942017},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.46386995911598206},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.39688557386398315},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.33633893728256226},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07197168469429016},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.06480059027671814},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp40776.2020.9053423","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053423","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 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":25,"referenced_works":["https://openalex.org/W1268391996","https://openalex.org/W1502922572","https://openalex.org/W1503398984","https://openalex.org/W1510073064","https://openalex.org/W1540155273","https://openalex.org/W1563088657","https://openalex.org/W1746819321","https://openalex.org/W1973650920","https://openalex.org/W1981552604","https://openalex.org/W1986280275","https://openalex.org/W2014158063","https://openalex.org/W2050297026","https://openalex.org/W2102955192","https://openalex.org/W2104128541","https://openalex.org/W2108995755","https://openalex.org/W2112081648","https://openalex.org/W2171930301","https://openalex.org/W2785772026","https://openalex.org/W2970255251","https://openalex.org/W2974489170","https://openalex.org/W3104887532","https://openalex.org/W4211049957","https://openalex.org/W6629804754","https://openalex.org/W6675374405","https://openalex.org/W6685488413"],"related_works":["https://openalex.org/W747394405","https://openalex.org/W1987236514","https://openalex.org/W174653542","https://openalex.org/W2381153750","https://openalex.org/W2213164457","https://openalex.org/W2157272197","https://openalex.org/W2246292763","https://openalex.org/W75679243","https://openalex.org/W1977826994","https://openalex.org/W4240247759"],"abstract_inverted_index":{"Kernel":[0],"regression":[1,20,93],"problem":[2],"with":[3,36],"autocorrelation":[4,31],"prior":[5],"is":[6,47,71],"discussed":[7],"in":[8,21,90],"this":[9],"paper.":[10],"We":[11,60],"revealed":[12],"the":[13,17,24,29,37,41,50,64,67,74,87,91],"optimal":[14,38,75],"model":[15,39],"of":[16,23,40,57,66,82,86],"kernel":[18,92],"ridge":[19],"terms":[22],"expected":[25,68],"generalization":[26],"error":[27],"under":[28],"assumed":[30],"prior.":[32],"This":[33],"result":[34],"agrees":[35],"Gaussian":[42],"process":[43],"regression,":[44],"whose":[45],"optimality":[46],"specified":[48],"by":[49,53],"conditional":[51],"expectation":[52],"a":[54,79],"given":[55],"set":[56],"training":[58],"samples.":[59],"also":[61],"proved":[62],"that":[63],"minimizer":[65],"cross-validation":[69,88],"criterion":[70],"reduced":[72],"to":[73],"model,":[76],"which":[77],"gives":[78],"novel":[80],"aspect":[81],"nonasymptotic":[83],"theoretical":[84],"justification":[85],"technique":[89],"problem.":[94]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}