{"id":"https://openalex.org/W2920342961","doi":"https://doi.org/10.1109/globalsip.2018.8646587","title":"THE FINITE SAMPLE PERFORMANCE OF DYNAMIC MODE DECOMPOSITION","display_name":"THE FINITE SAMPLE PERFORMANCE OF DYNAMIC MODE DECOMPOSITION","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2920342961","doi":"https://doi.org/10.1109/globalsip.2018.8646587","mag":"2920342961"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2018.8646587","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2018.8646587","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","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/A5062069277","display_name":"Arvind Prasadan","orcid":null},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Arvind Prasadan","raw_affiliation_strings":["Department of EECS, University of Michigan, Ann Arbor, MI"],"affiliations":[{"raw_affiliation_string":"Department of EECS, University of Michigan, Ann Arbor, MI","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071117473","display_name":"Raj Rao Nadakuditi","orcid":"https://orcid.org/0000-0002-5506-1631"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Raj Rao Nadakuditi","raw_affiliation_strings":["Department of EECS, University of Michigan, Ann Arbor, MI"],"affiliations":[{"raw_affiliation_string":"Department of EECS, University of Michigan, Ann Arbor, MI","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5062069277"],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":0.3016,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.58766268,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"1","issue":null,"first_page":"286","last_page":"290"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T11206","display_name":"Model Reduction and Neural Networks","score":0.9987999796867371,"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/T10534","display_name":"Structural Health Monitoring Techniques","score":0.9868000149726868,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T12560","display_name":"Nuclear Engineering Thermal-Hydraulics","score":0.9805999994277954,"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/dynamic-mode-decomposition","display_name":"Dynamic mode decomposition","score":0.7881726622581482},{"id":"https://openalex.org/keywords/autocorrelation","display_name":"Autocorrelation","score":0.7820780873298645},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6439262628555298},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5384329557418823},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.5216827392578125},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4888381361961365},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.469719797372818},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.46759480237960815},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4667209982872009},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.46605783700942993},{"id":"https://openalex.org/keywords/covariance","display_name":"Covariance","score":0.4422132670879364},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.420291930437088},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.41228151321411133},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3555246591567993},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08707204461097717},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.07733282446861267}],"concepts":[{"id":"https://openalex.org/C2777032711","wikidata":"https://www.wikidata.org/wiki/Q5318993","display_name":"Dynamic mode decomposition","level":2,"score":0.7881726622581482},{"id":"https://openalex.org/C5297727","wikidata":"https://www.wikidata.org/wiki/Q786970","display_name":"Autocorrelation","level":2,"score":0.7820780873298645},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6439262628555298},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5384329557418823},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.5216827392578125},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4888381361961365},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.469719797372818},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.46759480237960815},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4667209982872009},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.46605783700942993},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.4422132670879364},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.420291930437088},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41228151321411133},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3555246591567993},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08707204461097717},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.07733282446861267},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globalsip.2018.8646587","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2018.8646587","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","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":10,"referenced_works":["https://openalex.org/W1908611567","https://openalex.org/W2014356541","https://openalex.org/W2040690030","https://openalex.org/W2078898317","https://openalex.org/W2164954534","https://openalex.org/W2734338433","https://openalex.org/W2963006871","https://openalex.org/W3099803807","https://openalex.org/W3102306519","https://openalex.org/W6741473185"],"related_works":["https://openalex.org/W2597763219","https://openalex.org/W2014356541","https://openalex.org/W2168222921","https://openalex.org/W747394405","https://openalex.org/W2992578100","https://openalex.org/W4286695296","https://openalex.org/W3028908947","https://openalex.org/W2886934452","https://openalex.org/W1489099099","https://openalex.org/W2024369332"],"abstract_inverted_index":{"We":[0,28,81],"analyze":[1],"the":[2,17,22,32,35,60,72,77],"Dynamic":[3],"Mode":[4],"Decomposition":[5],"(DMD)":[6],"algorithm":[7],"as":[8,43],"applied":[9],"to":[10,68,95],"multivariate":[11,36],"time-series":[12],"data.":[13],"Our":[14],"analysis":[15],"reveals":[16],"critical":[18],"role":[19],"played":[20],"by":[21],"lag-one":[23,47,56],"cross-correlation,":[24],"or":[25],"cross-covariance,":[26],"terms.":[27],"show":[29],"that":[30,52],"when":[31],"rows":[33],"of":[34,46],"time":[37,50,79],"series":[38,51],"matrix":[39],"can":[40,92],"be":[41,93],"modeled":[42],"linear":[44],"combinations":[45],"uncorrelated":[48],"latent":[49,78],"have":[53],"a":[54,69],"non-zero":[55],"autocorrelation,":[57],"then":[58],"in":[59],"large":[61],"sample":[62],"limit,":[63],"DMD":[64,91],"perfectly":[65],"recovers,":[66],"up":[67],"column-wise":[70],"scaling,":[71],"mixing":[73],"matrix,":[74],"and":[75,88],"thus":[76],"series.":[80],"validate":[82],"our":[83],"findings":[84],"with":[85],"numerical":[86],"simulations,":[87],"demonstrate":[89],"how":[90],"used":[94],"unmix":[96],"mixed":[97],"audio":[98],"signals.":[99]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
