{"id":"https://openalex.org/W4401752620","doi":"https://doi.org/10.1109/spcom60851.2024.10631639","title":"Identification of Stochasticity by Matrix-Decomposition: Applied on Black Hole Data","display_name":"Identification of Stochasticity by Matrix-Decomposition: Applied on Black Hole Data","publication_year":2024,"publication_date":"2024-07-01","ids":{"openalex":"https://openalex.org/W4401752620","doi":"https://doi.org/10.1109/spcom60851.2024.10631639"},"language":"en","primary_location":{"id":"doi:10.1109/spcom60851.2024.10631639","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/spcom60851.2024.10631639","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Signal Processing and Communications (SPCOM)","raw_type":"proceedings-article"},"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/A5113342815","display_name":"Chakka Sai Pradeep","orcid":null},"institutions":[{"id":"https://openalex.org/I181514455","display_name":"International Institute of Information Technology Bangalore","ror":"https://ror.org/05h9eqy10","country_code":"IN","type":"education","lineage":["https://openalex.org/I181514455"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chakka Sai Pradeep","raw_affiliation_strings":["International Institute of Information Technology,Bangalore,India,560100"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"International Institute of Information Technology,Bangalore,India,560100","institution_ids":["https://openalex.org/I181514455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014371833","display_name":"Neelam Sinha","orcid":"https://orcid.org/0000-0001-8164-8412"},"institutions":[{"id":"https://openalex.org/I59270414","display_name":"Indian Institute of Science Bangalore","ror":"https://ror.org/04dese585","country_code":"IN","type":"education","lineage":["https://openalex.org/I59270414"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Neelam Sinha","raw_affiliation_strings":["Center for Brain Research, Indian Institute of Science,Bangalore,India,560012"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Brain Research, Indian Institute of Science,Bangalore,India,560012","institution_ids":["https://openalex.org/I59270414"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4171,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.53894909,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"355","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9979000091552734,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9979000091552734,"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.9912999868392944,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9483000040054321,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.6297474503517151},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.567038893699646},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5515673756599426},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5302996635437012},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.48398566246032715},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.20667940378189087},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.15112170577049255},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.09045776724815369}],"concepts":[{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.6297474503517151},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.567038893699646},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5515673756599426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5302996635437012},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.48398566246032715},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.20667940378189087},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.15112170577049255},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.09045776724815369},{"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/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"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/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/spcom60851.2024.10631639","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/spcom60851.2024.10631639","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Signal Processing and Communications (SPCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1541188138","https://openalex.org/W2028620594","https://openalex.org/W2050785676","https://openalex.org/W2165413996","https://openalex.org/W2475109240","https://openalex.org/W2524083015","https://openalex.org/W2785216882","https://openalex.org/W2892035503","https://openalex.org/W2950312697","https://openalex.org/W3101784999","https://openalex.org/W4234345389","https://openalex.org/W4287815523","https://openalex.org/W4372266952","https://openalex.org/W6640176828","https://openalex.org/W6771988894"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W4402327032","https://openalex.org/W2382290278"],"abstract_inverted_index":{"Timeseries":[0],"classification":[1],"as":[2],"non-stochastic":[3],"(structured)":[4],"or":[5,37],"stochastic":[6],"(lacking":[7],"structure)":[8],"helps":[9],"understand":[10],"underlying":[11],"dynamics,":[12],"in":[13,40,153,164,168],"several":[14],"domains.":[15],"One":[16],"of":[17,21,128,140,148],"the":[18,27,82,109,172],"novel":[19],"contributions":[20],"this":[22],"work":[23],"is":[24,78,111,122,166],"to":[25,72,96,180],"utilize":[26],"well-known":[28],"Principal":[29],"Component":[30],"Analysis":[31],"(PCA)":[32],"for":[33],"identifying":[34],"structured":[35],"behavior":[36],"lack":[38],"thereof,":[39],"a":[41,47,100],"timeseries.":[42],"For":[43,99],"classification,":[44],"we":[45],"propose":[46],"two-legged":[48],"matrix":[49],"decomposition-based":[50],"algorithm":[51],"utilizing":[52],"two":[53],"complementary":[54],"techniques":[55],"(SVD":[56],"and":[57,105,157],"PCA).":[58],"SVD-Ieg":[59],"performs":[60],"topological":[61],"analysis":[62],"(Betti":[63],"numbers)":[64],"on":[65,81,92,124],"singular":[66],"vectors":[67],"containing":[68],"temporal":[69,145,170,174],"information,":[70],"leading":[71],"SVD-Iabel.":[73],"Parallely,":[74],"temporal-ordering":[75],"agnostic":[76],"PCA":[77],"performed":[79],"hierarchically":[80],"timeseries,":[83,102],"computing":[84],"proposed":[85,120],"Eigen-ratio":[86],"based":[87,159,188],"features":[88],"that":[89],"quantify":[90],"structure,":[91],"progressively":[93],"shorter":[94],"time-windows,":[95],"obtain":[97],"PCA-Iabel.":[98],"given":[101],"if":[103],"SVD-Iabel":[104],"PCA-label":[106],"concur,":[107],"then":[108],"label":[110],"retained;":[112],"else":[113],"deemed":[114,176],"\u201cUncertain\u201d,":[115],"requiring":[116],"further":[117,191],"investigation.":[118],"The":[119],"methodology":[121],"illustrated":[123],"publicly":[125],"available":[126],"data":[127],"black":[129],"hole":[130],"GRS":[131],"1915+105,":[132],"obtained":[133,149],"from":[134],"RXTE":[135],"satellite,":[136],"with":[137,151],"average":[138],"length":[139],"25000":[141],"datapoints,":[142],"across":[143],"12":[144],"classes.":[146],"Comparison":[147],"results":[150],"those":[152],"literature":[154],"using":[155,184],"traditional":[156],"deep-learning":[158,187],"methods":[160],"are":[161],"presented.":[162],"Concurrence":[163],"labels":[165],"shown":[167],"11":[169],"classes;":[171],"one":[173],"class":[175],"\u201cUncertain\u201d":[177],"turns":[178],"out":[179],"be":[181],"differently":[182],"labelled":[183],"yet":[185],"another":[186],"approach,":[189],"warranting":[190],"investigation":[192],"into":[193],"its":[194],"characteristics.":[195]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
