{"id":"https://openalex.org/W2562011867","doi":"https://doi.org/10.1109/eusipco.2016.7760261","title":"Kronecker covariance sketching for spatial-temporal data","display_name":"Kronecker covariance sketching for spatial-temporal data","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2562011867","doi":"https://doi.org/10.1109/eusipco.2016.7760261","mag":"2562011867"},"language":"en","primary_location":{"id":"doi:10.1109/eusipco.2016.7760261","doi":"https://doi.org/10.1109/eusipco.2016.7760261","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eusipco.2016.7760261","pdf_url":null,"source":{"id":"https://openalex.org/S4363607788","display_name":"European Signal Processing Conference (EUSIPCO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2016 24th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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/A5053809095","display_name":"Yuejie Chi","orcid":"https://orcid.org/0000-0002-6766-5459","observed_orcids":["https://orcid.org/0000-0002-6766-5459"]},"institutions":[{"id":"https://openalex.org/I17974374","display_name":"Universidade de S\u00e3o Paulo","ror":"https://ror.org/036rp1748","country_code":"BR","type":"education","lineage":["https://openalex.org/I17974374"]},{"id":"https://openalex.org/I192513696","display_name":"National University of San Marcos","ror":"https://ror.org/006vs7897","country_code":"PE","type":"education","lineage":["https://openalex.org/I192513696"]}],"countries":["BR","PE"],"is_corresponding":true,"raw_author_name":"Yuejie Chi","raw_affiliation_strings":["Universidad Nacional Mayor de San Marcos","Universidade de Sao Paulo"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Universidad Nacional Mayor de San Marcos","institution_ids":["https://openalex.org/I192513696"]},{"raw_affiliation_string":"Universidade de Sao Paulo","institution_ids":["https://openalex.org/I17974374"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5053809095"],"corresponding_institution_ids":["https://openalex.org/I17974374","https://openalex.org/I192513696"],"apc_list":null,"apc_paid":null,"fwci":2.4325,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.90763582,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"316","last_page":"320"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.989300012588501,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.989300012588501,"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/T10792","display_name":"Matrix Theory and Algorithms","score":0.0010000000474974513,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/T10891","display_name":"Radar Systems and Signal Processing","score":0.0008999999845400453,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/spatio-temporal-data","display_name":"spatio-temporal data","score":0.9580000042915344},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"dimensionality reduction","score":0.8740000128746033},{"id":"https://openalex.org/keywords/magnitude-only-measurements","display_name":"magnitude-only measurements","score":0.8410000205039978},{"id":"https://openalex.org/keywords/covariance-matrix-estimation","display_name":"covariance matrix estimation","score":0.8659999966621399},{"id":"https://openalex.org/keywords/convex-relaxation","display_name":"convex relaxation","score":0.9919999837875366},{"id":"https://openalex.org/keywords/wideband-spectrum-sensing","display_name":"wideband spectrum sensing","score":0.8920000195503235},{"id":"https://openalex.org/keywords/optical-imaging","display_name":"optical imaging","score":0.9929999709129333}],"concepts":[{"id":"https://openalex.org/C148893098","wikidata":"https://www.wikidata.org/wiki/Q7295778","display_name":"Rational quadratic covariance function","level":5,"score":0.8149071931838989},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.783706784248352},{"id":"https://openalex.org/C46030957","wikidata":"https://www.wikidata.org/wiki/Q1238125","display_name":"Kronecker product","level":3,"score":0.7310630679130554},{"id":"https://openalex.org/C118006245","wikidata":"https://www.wikidata.org/wiki/Q6792079","display_name":"Mat\u00e9rn covariance function","level":5,"score":0.6750319600105286},{"id":"https://openalex.org/C180877172","wikidata":"https://www.wikidata.org/wiki/Q5401390","display_name":"Estimation of covariance matrices","level":3,"score":0.6704127788543701},{"id":"https://openalex.org/C137250428","wikidata":"https://www.wikidata.org/wiki/Q5178897","display_name":"Covariance function","level":3,"score":0.6318747401237488},{"id":"https://openalex.org/C83042196","wikidata":"https://www.wikidata.org/wiki/Q5178898","display_name":"Covariance intersection","level":4,"score":0.6063507199287415},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.581406831741333},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5744982957839966},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5070338249206543},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.46929460763931274},{"id":"https://openalex.org/C147710293","wikidata":"https://www.wikidata.org/wiki/Q849428","display_name":"Toeplitz matrix","level":2,"score":0.4411745071411133},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.42539939284324646},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.417595237493515},{"id":"https://openalex.org/C39482219","wikidata":"https://www.wikidata.org/wiki/Q192826","display_name":"Kronecker delta","level":2,"score":0.41449910402297974},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3693219721317291},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.25919634103775024},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17712664604187012},{"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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/eusipco.2016.7760261","doi":"https://doi.org/10.1109/eusipco.2016.7760261","is_oa":false,"landing_page_url":"https://doi.org/10.1109/eusipco.2016.7760261","pdf_url":null,"source":{"id":"https://openalex.org/S4363607788","display_name":"European Signal Processing Conference (EUSIPCO)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2016 24th European Signal Processing Conference (EUSIPCO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1581554073","https://openalex.org/W1589221493","https://openalex.org/W1996076814","https://openalex.org/W2001380973","https://openalex.org/W2003571173","https://openalex.org/W2032922604","https://openalex.org/W2067157583","https://openalex.org/W2072987322","https://openalex.org/W2076606053","https://openalex.org/W2077813311","https://openalex.org/W2078537874","https://openalex.org/W2081540728","https://openalex.org/W2103193999","https://openalex.org/W2115671952","https://openalex.org/W2118550318","https://openalex.org/W2133105246","https://openalex.org/W2155413294","https://openalex.org/W2168746287","https://openalex.org/W2211589247","https://openalex.org/W2279145679","https://openalex.org/W2594833489"],"related_works":["https://openalex.org/W4319586197","https://openalex.org/W2243890939","https://openalex.org/W2115671952","https://openalex.org/W1964391232","https://openalex.org/W2520594112","https://openalex.org/W2728480621","https://openalex.org/W1589153704","https://openalex.org/W2293708310","https://openalex.org/W2133105246","https://openalex.org/W7095209305"],"abstract_inverted_index":{"Covariance":[0],"sketching":[1,34],"has":[2],"been":[3],"recently":[4,51],"introduced":[5,52],"as":[6,82],"an":[7],"effective":[8],"strategy":[9],"to":[10,19,79,91,119],"reduce":[11],"the":[12,17,24,46,93,101,108,121,124],"data":[13],"dimensionality":[14],"without":[15],"sacrificing":[16],"ability":[18],"reconstruct":[20],"second-order":[21],"statistics":[22],"of":[23,123],"data.":[25],"In":[26],"this":[27],"paper,":[28],"we":[29],"propose":[30,90],"a":[31],"novel":[32],"covariance":[33,43,94,110],"scheme":[35,58],"with":[36],"reduced":[37],"complexity":[38],"for":[39,72],"spatial-temporal":[40],"data,":[41],"whose":[42],"matrices":[44],"satisfy":[45],"Kronecker":[47],"product":[48],"expansion":[49],"model":[50],"by":[53],"Tsiligkaridis":[54],"and":[55,86,106],"Hero.":[56],"Our":[57],"is":[59,70,77,104,111],"based":[60,96],"on":[61,97],"quadratic":[62],"sampling":[63],"that":[64],"only":[65],"requires":[66],"magnitude":[67],"measurements,":[68],"hence":[69],"appealing":[71],"applications":[73],"when":[74,100,107],"phase":[75],"information":[76],"difficult":[78],"obtain,":[80],"such":[81],"wideband":[83],"spectrum":[84],"sensing":[85],"optical":[87],"imaging.":[88],"We":[89],"estimate":[92],"matrix":[95],"convex":[98],"relaxation":[99],"separation":[102],"rank":[103],"small,":[105],"temporal":[109],"additionally":[112],"Toeplitz":[113],"structured.":[114],"Numerical":[115],"examples":[116],"are":[117],"provided":[118],"demonstrate":[120],"effectiveness":[122],"proposed":[125],"scheme.":[126]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2017,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2017-01-06T00:00:00"}