{"id":"https://openalex.org/W2243093525","doi":"https://doi.org/10.1109/icdm.2015.38","title":"Multi-level Approximate Spectral Clustering","display_name":"Multi-level Approximate Spectral Clustering","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2243093525","doi":"https://doi.org/10.1109/icdm.2015.38","mag":"2243093525"},"language":"en","primary_location":{"id":"doi:10.1109/icdm.2015.38","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm.2015.38","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Mining","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/A5100318892","display_name":"Lijun Wang","orcid":"https://orcid.org/0000-0003-2538-8358"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lijun Wang","raw_affiliation_strings":["Department of Computer Science, Wayne State University, Detroit, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Wayne State University, Detroit, USA","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112047383","display_name":"Ming Dong","orcid":"https://orcid.org/0000-0002-0616-4431"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ming Dong","raw_affiliation_strings":["Department of Computer Science, Wayne State University, Detroit, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Wayne State University, Detroit, USA","institution_ids":["https://openalex.org/I185443292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025168533","display_name":"Alexander Kotov","orcid":"https://orcid.org/0000-0002-9872-6605"},"institutions":[{"id":"https://openalex.org/I185443292","display_name":"Wayne State University","ror":"https://ror.org/01070mq45","country_code":"US","type":"education","lineage":["https://openalex.org/I185443292"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander Kotov","raw_affiliation_strings":["Department of Computer Science, Wayne State University, Detroit, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Wayne State University, Detroit, USA","institution_ids":["https://openalex.org/I185443292"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1872,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.60421999,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9979000091552734,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9979000091552734,"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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9865000247955322,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.7717924118041992},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7662979364395142},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6050996780395508},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.5865755677223206},{"id":"https://openalex.org/keywords/laplacian-matrix","display_name":"Laplacian matrix","score":0.5806381702423096},{"id":"https://openalex.org/keywords/correctness","display_name":"Correctness","score":0.550825834274292},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4898988604545593},{"id":"https://openalex.org/keywords/low-rank-approximation","display_name":"Low-rank approximation","score":0.46264684200286865},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.4329334497451782},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4237775206565857},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.3757816255092621},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3132137954235077},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3113115429878235},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2542944550514221},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.2267453670501709},{"id":"https://openalex.org/keywords/hankel-matrix","display_name":"Hankel matrix","score":0.10811442136764526}],"concepts":[{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.7717924118041992},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7662979364395142},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6050996780395508},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.5865755677223206},{"id":"https://openalex.org/C115178988","wikidata":"https://www.wikidata.org/wiki/Q772067","display_name":"Laplacian matrix","level":3,"score":0.5806381702423096},{"id":"https://openalex.org/C55439883","wikidata":"https://www.wikidata.org/wiki/Q360812","display_name":"Correctness","level":2,"score":0.550825834274292},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4898988604545593},{"id":"https://openalex.org/C90199385","wikidata":"https://www.wikidata.org/wiki/Q6692777","display_name":"Low-rank approximation","level":3,"score":0.46264684200286865},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.4329334497451782},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4237775206565857},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3757816255092621},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3132137954235077},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3113115429878235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2542944550514221},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.2267453670501709},{"id":"https://openalex.org/C25023664","wikidata":"https://www.wikidata.org/wiki/Q1575637","display_name":"Hankel matrix","level":2,"score":0.10811442136764526},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icdm.2015.38","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icdm.2015.38","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Data Mining","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.708.2193","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.708.2193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cs.wayne.edu/kotov/docs/wang-icdm15.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W145504188","https://openalex.org/W204133272","https://openalex.org/W1564611420","https://openalex.org/W1568377519","https://openalex.org/W1766432634","https://openalex.org/W1967934524","https://openalex.org/W1970576574","https://openalex.org/W2006373179","https://openalex.org/W2042465463","https://openalex.org/W2061922307","https://openalex.org/W2072738603","https://openalex.org/W2109722477","https://openalex.org/W2113065518","https://openalex.org/W2116810533","https://openalex.org/W2121947440","https://openalex.org/W2134370969","https://openalex.org/W2135957668","https://openalex.org/W2159526734","https://openalex.org/W2160840682","https://openalex.org/W3004219628","https://openalex.org/W3134067276","https://openalex.org/W4285719527","https://openalex.org/W6608299420","https://openalex.org/W6683673261"],"related_works":["https://openalex.org/W2607797544","https://openalex.org/W2902850141","https://openalex.org/W2197112176","https://openalex.org/W2885900820","https://openalex.org/W1556852487","https://openalex.org/W2136935487","https://openalex.org/W1955548541","https://openalex.org/W2265158691","https://openalex.org/W2032317191","https://openalex.org/W2964122166"],"abstract_inverted_index":{"Clustering":[0],"is":[1,21,27],"a":[2,22,71,107,141],"task":[3],"of":[4,29,33,66,73,148,167],"finding":[5],"natural":[6],"groups":[7],"in":[8,152],"datasets":[9,43],"based":[10],"on":[11],"measured":[12],"or":[13],"perceived":[14],"similarity":[15],"between":[16],"data":[17],"points.":[18],"Spectral":[19,59],"clustering":[20,61,177],"well-known":[23,174],"graph-theoretic":[24],"approach,":[25],"which":[26,116],"capable":[28],"capturing":[30],"non-convex":[31],"geometries":[32],"datasets.":[34,68],"However,":[35],"it":[36,132],"generally":[37],"becomes":[38],"infeasible":[39],"for":[40,90,110,137],"analyzing":[41],"large":[42,67],"due":[44],"to":[45,62,79,154,172],"relatively":[46],"high":[47],"time":[48],"and":[49,83,94,102,112,125,157],"space":[50],"complexity.":[51,159],"In":[52,130],"this":[53],"paper,":[54],"we":[55,144,163],"propose":[56],"Multi-level":[57],"Approximate":[58],"(MAS)":[60],"enable":[63],"efficient":[64],"analysis":[65,147],"By":[69],"integrating":[70],"series":[72],"low-rank":[74,127],"matrix":[75,82,93],"approximations":[76,78],"(i.e.,":[77],"the":[80,91,95,168],"affinity":[81],"its":[84,149,155],"subspace,":[85],"as":[86,88],"well":[87],"those":[89],"Laplacian":[92,96],"subspace),":[97],"MAS":[98,105],"achieves":[99],"great":[100],"computational":[101,158],"spacial":[103],"efficiency.":[104],"provides":[106],"general":[108],"framework":[109],"fast":[111,122],"accurate":[113],"spectral":[114,176],"clustering,":[115],"works":[117],"with":[118],"any":[119],"kernels,":[120],"various":[121],"sampling":[123],"strategies":[124],"different":[126],"approximation":[128,150],"algorithms.":[129,178],"addition,":[131],"can":[133],"be":[134],"easily":[135],"extended":[136],"distributed":[138],"computing.":[139],"From":[140],"theoretical":[142],"perspective,":[143],"provide":[145],"rigorous":[146],"error":[151],"addition":[153],"correctness":[156],"Through":[160],"extensive":[161],"experiments":[162],"demonstrate":[164],"superior":[165],"performance":[166],"proposed":[169],"method":[170],"relative":[171],"several":[173],"approximate":[175]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}