{"id":"https://openalex.org/W2607797544","doi":"https://doi.org/10.1109/icpr.2016.7899979","title":"Spectral sparsification in spectral clustering","display_name":"Spectral sparsification in spectral clustering","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2607797544","doi":"https://doi.org/10.1109/icpr.2016.7899979","mag":"2607797544"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2016.7899979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","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/A5044288535","display_name":"Alireza Chakeri","orcid":null},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alireza Chakeri","raw_affiliation_strings":["Computer Science and Engineering Department, University of South Florida, Tampa, Florida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering Department, University of South Florida, Tampa, Florida","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023093696","display_name":"Hamidreza Farhidzadeh","orcid":null},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hamidreza Farhidzadeh","raw_affiliation_strings":["Computer Science and Engineering Department, University of South Florida, Tampa, Florida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering Department, University of South Florida, Tampa, Florida","institution_ids":["https://openalex.org/I2613432"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000168449","display_name":"Lawrence Hall","orcid":"https://orcid.org/0000-0002-7898-8456"},"institutions":[{"id":"https://openalex.org/I2613432","display_name":"University of South Florida","ror":"https://ror.org/032db5x82","country_code":"US","type":"education","lineage":["https://openalex.org/I2613432"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lawrence O. Hall","raw_affiliation_strings":["Computer Science and Engineering Department, University of South Florida, Tampa, Florida"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science and Engineering Department, University of South Florida, Tampa, Florida","institution_ids":["https://openalex.org/I2613432"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I2613432"],"apc_list":null,"apc_paid":null,"fwci":0.1941,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.55253041,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2301","last_page":"2306"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9972000122070312,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9972000122070312,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9958999752998352,"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/T10057","display_name":"Face and Expression Recognition","score":0.9950000047683716,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/spectral-clustering","display_name":"Spectral clustering","score":0.8883764743804932},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.8041499853134155},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6959391832351685},{"id":"https://openalex.org/keywords/spectral-graph-theory","display_name":"Spectral graph theory","score":0.5307557582855225},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5004868507385254},{"id":"https://openalex.org/keywords/laplace-operator","display_name":"Laplace operator","score":0.47026190161705017},{"id":"https://openalex.org/keywords/spectral-properties","display_name":"Spectral properties","score":0.45988520979881287},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4555779993534088},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.45064669847488403},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.4458089768886566},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4256178140640259},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33673352003097534},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2452932596206665},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.12784633040428162},{"id":"https://openalex.org/keywords/graph-power","display_name":"Graph power","score":0.1007724404335022},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.08929213881492615}],"concepts":[{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.8883764743804932},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.8041499853134155},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6959391832351685},{"id":"https://openalex.org/C74003402","wikidata":"https://www.wikidata.org/wiki/Q3180727","display_name":"Spectral graph theory","level":5,"score":0.5307557582855225},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5004868507385254},{"id":"https://openalex.org/C165700671","wikidata":"https://www.wikidata.org/wiki/Q203484","display_name":"Laplace operator","level":2,"score":0.47026190161705017},{"id":"https://openalex.org/C2985906921","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Spectral properties","level":2,"score":0.45988520979881287},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4555779993534088},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.45064669847488403},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.4458089768886566},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4256178140640259},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33673352003097534},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2452932596206665},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.12784633040428162},{"id":"https://openalex.org/C149530733","wikidata":"https://www.wikidata.org/wiki/Q5597091","display_name":"Graph power","level":4,"score":0.1007724404335022},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.08929213881492615},{"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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icpr.2016.7899979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899979","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W100944330","https://openalex.org/W1577058187","https://openalex.org/W1587744656","https://openalex.org/W1841961408","https://openalex.org/W1916091028","https://openalex.org/W1972098431","https://openalex.org/W1972969203","https://openalex.org/W1983193888","https://openalex.org/W1997315949","https://openalex.org/W2011655084","https://openalex.org/W2034566398","https://openalex.org/W2036978226","https://openalex.org/W2051540665","https://openalex.org/W2062291655","https://openalex.org/W2098983955","https://openalex.org/W2116810533","https://openalex.org/W2121947440","https://openalex.org/W2125464731","https://openalex.org/W2126337883","https://openalex.org/W2129575457","https://openalex.org/W2140802988","https://openalex.org/W2153934661","https://openalex.org/W2165874743","https://openalex.org/W2280229040","https://openalex.org/W2295256067","https://openalex.org/W2319660501","https://openalex.org/W2400028470","https://openalex.org/W6604134081","https://openalex.org/W6683047094","https://openalex.org/W6684578312","https://openalex.org/W6713140404"],"related_works":["https://openalex.org/W3004345458","https://openalex.org/W2136935487","https://openalex.org/W2032317191","https://openalex.org/W2902850141","https://openalex.org/W2265158691","https://openalex.org/W1955548541","https://openalex.org/W2885900820","https://openalex.org/W2613055102","https://openalex.org/W2607797544","https://openalex.org/W3106442811"],"abstract_inverted_index":{"Graph":[0],"spectral":[1,84,100,108,153,165,176],"clustering":[2,17,177,185],"algorithms":[3],"have":[4,24],"been":[5],"shown":[6],"to":[7,102,194],"be":[8],"effective":[9,172],"in":[10,27,92],"finding":[11],"clusters":[12],"and":[13,31,119,126,138],"generally":[14,53],"outperform":[15],"traditional":[16],"algorithms,":[18],"such":[19,62],"as":[20,63],"k-means.":[21],"However,":[22],"they":[23,69],"scalibility":[25],"issues":[26],"both":[28],"memory":[29],"usage":[30],"computational":[32],"time.":[33],"To":[34],"overcome":[35],"these":[36],"limitations,":[37],"the":[38,42,59,64,73,97,104,133,143,152,156,161,164,175,184,195,198],"common":[39],"approaches":[40],"sparsify":[41,103],"similarity":[43],"matrix":[44],"by":[45,171],"zeroing":[46],"out":[47],"some":[48],"of":[49,82,99,136,145,155,163,197],"its":[50,83],"elements.":[51],"They":[52],"consider":[54],"local":[55],"neighborhood":[56],"relationships":[57],"between":[58],"data":[60],"instances":[61],"k-nearest":[65],"neighbor":[66],"method.":[67],"Although,":[68],"eventually":[70],"work":[71],"with":[72,86,116,132],"Laplacian":[74,105],"matrix,":[75],"there":[76],"is":[77,123],"no":[78],"evidence":[79],"about":[80],"preservation":[81],"properties":[85,154],"approximation":[87],"guarantees.":[88],"As":[89],"a":[90,112,128],"result,":[91],"this":[93],"paper,":[94],"we":[95,181],"adopt":[96],"idea":[98],"sparsification":[101,109,166],"matrix.":[106],"A":[107],"algorithm":[110],"takes":[111],"dense":[113],"graph":[114,130],"G":[115],"n":[117],"vertices":[118,137],"m":[120],"edges":[121],"(that":[122],"usually":[124],"O(n2)),":[125],"returns":[127],"new":[129],"H":[131],"same":[134],"set":[135],"many":[139],"fewer":[140],"edges,":[141],"on":[142,169,174],"order":[144],"O(n":[146],"log":[147],"n),":[148],"that":[149,183],"approximately":[150],"preserves":[151],"input":[157],"graph.":[158],"We":[159],"study":[160],"effect":[162],"method":[167],"based":[168],"sampling":[170],"resistance":[173],"outputs.":[178],"Through":[179],"experiments,":[180],"show":[182],"results":[186,196],"obtained":[187],"from":[188],"sparsified":[189],"graphs":[190],"are":[191],"very":[192],"similar":[193],"original":[199],"non-sparsified":[200],"graphs.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}