{"id":"https://openalex.org/W2289393519","doi":"https://doi.org/10.1109/acssc.2015.7421091","title":"Change-point estimation of high-dimensional streaming data via sketching","display_name":"Change-point estimation of high-dimensional streaming data via sketching","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2289393519","doi":"https://doi.org/10.1109/acssc.2015.7421091","mag":"2289393519"},"language":"en","primary_location":{"id":"doi:10.1109/acssc.2015.7421091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2015.7421091","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 49th Asilomar Conference on Signals, Systems and Computers","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/A5053809095","display_name":"Yuejie Chi","orcid":"https://orcid.org/0000-0002-6766-5459"},"institutions":[{"id":"https://openalex.org/I52357470","display_name":"The Ohio State University","ror":"https://ror.org/00rs6vg23","country_code":"US","type":"education","lineage":["https://openalex.org/I52357470"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuejie Chi","raw_affiliation_strings":["Electrical and Computer Engineering, The Ohio State University, Columbus, OH"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, The Ohio State University, Columbus, OH","institution_ids":["https://openalex.org/I52357470"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100645159","display_name":"Yihong Wu","orcid":"https://orcid.org/0000-0001-9239-7671"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yihong Wu","raw_affiliation_strings":["Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":null,"first_page":"102","last_page":"106"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9980999827384949,"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":0.9980999827384949,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T10136","display_name":"Statistical Methods and Inference","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7204320430755615},{"id":"https://openalex.org/keywords/change-detection","display_name":"Change detection","score":0.6849005222320557},{"id":"https://openalex.org/keywords/sketch","display_name":"Sketch","score":0.6107648015022278},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.49513188004493713},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.47177672386169434},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.46494048833847046},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.46151989698410034},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4130551517009735},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4093289375305176},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3183572590351105},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20573970675468445},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.10648852586746216}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7204320430755615},{"id":"https://openalex.org/C203595873","wikidata":"https://www.wikidata.org/wiki/Q25389927","display_name":"Change detection","level":2,"score":0.6849005222320557},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.6107648015022278},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.49513188004493713},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.47177672386169434},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.46494048833847046},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.46151989698410034},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4130551517009735},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4093289375305176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3183572590351105},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20573970675468445},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.10648852586746216},{"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},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acssc.2015.7421091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2015.7421091","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 49th Asilomar Conference on Signals, Systems and Computers","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":20,"referenced_works":["https://openalex.org/W1965972569","https://openalex.org/W1987371344","https://openalex.org/W1997010704","https://openalex.org/W2038869097","https://openalex.org/W2042543554","https://openalex.org/W2051245758","https://openalex.org/W2075406189","https://openalex.org/W2089700840","https://openalex.org/W2130041555","https://openalex.org/W2133105246","https://openalex.org/W2134939827","https://openalex.org/W2149247444","https://openalex.org/W2151825876","https://openalex.org/W2169811219","https://openalex.org/W2206504153","https://openalex.org/W2296616510","https://openalex.org/W2611328865","https://openalex.org/W2964167436","https://openalex.org/W4250955649","https://openalex.org/W6682550672"],"related_works":["https://openalex.org/W2378994405","https://openalex.org/W2385974820","https://openalex.org/W2373478030","https://openalex.org/W2378679551","https://openalex.org/W3149739944","https://openalex.org/W2392363776","https://openalex.org/W2063051341","https://openalex.org/W2591066345","https://openalex.org/W1494563618","https://openalex.org/W2357022711"],"abstract_inverted_index":{"Change-point":[0],"detection":[1,13,48],"is":[2,21,95],"of":[3,39,73,111,140],"great":[4],"interest":[5],"in":[6,125],"applications":[7],"such":[8,88],"as":[9,89],"target":[10],"tracking,":[11],"anomaly":[12],"and":[14,37,49,91,105],"trend":[15],"filtering.":[16],"In":[17],"many":[18],"cases,":[19],"it":[20,30,134],"also":[22],"desirable":[23],"to":[24,129],"localize":[25],"the":[26,34,81,92,99],"change-":[27],"point,":[28],"if":[29],"exists.":[31],"Motivated":[32],"by":[33,67],"unprecedented":[35],"scale":[36],"rate":[38],"modern":[40],"high-dimensional":[41],"streaming":[42],"data,":[43],"we":[44],"propose":[45],"a":[46,59,70,108],"change-point":[47],"estimation":[50],"procedure":[51,121],"based":[52,114],"on":[53,115,137],"data":[54,65],"sketching,":[55],"which":[56],"only":[57],"requires":[58],"single":[60],"sketch":[61],"per":[62],"high-":[63],"dimensional":[64],"vector,":[66],"cyclically":[68],"applying":[69],"small":[71,109,138],"set":[72],"Gaussian":[74],"sketching":[75,112],"vectors.":[76],"We":[77],"demonstrate":[78],"that":[79],"when":[80],"underlying":[82],"changes":[83],"exhibit":[84],"certain":[85],"low-dimensional":[86],"structures,":[87],"sparsity,":[90],"signal-to-noise":[93],"ratio":[94],"not":[96],"too":[97],"small,":[98],"change-points":[100],"can":[101,122],"be":[102,123],"reliably":[103],"detected":[104],"located":[106],"with":[107],"number":[110],"vectors":[113],"filtering":[116],"via":[117],"convex":[118],"optimization.":[119],"Our":[120],"implemented":[124],"an":[126],"online":[127],"fashion":[128],"handle":[130],"multiple":[131],"change-points,":[132],"since":[133],"sequentially":[135],"operates":[136],"windows":[139],"observations.":[141]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}