{"id":"https://openalex.org/W2550718412","doi":"https://doi.org/10.1109/ijcnn.2016.7727698","title":"Theoretical analysis of the Minimum Sum of Squared Similarities sampling for Nystr\u00f6m-based spectral clustering","display_name":"Theoretical analysis of the Minimum Sum of Squared Similarities sampling for Nystr\u00f6m-based spectral clustering","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2550718412","doi":"https://doi.org/10.1109/ijcnn.2016.7727698","mag":"2550718412"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727698","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727698","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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/A5055394860","display_name":"Djallel Bouneffouf","orcid":"https://orcid.org/0000-0003-3342-7513"},"institutions":[{"id":"https://openalex.org/I4210133112","display_name":"Canada's Michael Smith Genome Sciences Centre","ror":"https://ror.org/0333j0897","country_code":"CA","type":"funder","lineage":["https://openalex.org/I4210133112"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Djallel Bouneffouf","raw_affiliation_strings":["British Columbia Cancer Agency, Canada's Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada"],"affiliations":[{"raw_affiliation_string":"British Columbia Cancer Agency, Canada's Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada","institution_ids":["https://openalex.org/I4210133112"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017539699","display_name":"\u0130nan\u00e7 Birol","orcid":"https://orcid.org/0000-0003-0950-7839"},"institutions":[{"id":"https://openalex.org/I4210133112","display_name":"Canada's Michael Smith Genome Sciences Centre","ror":"https://ror.org/0333j0897","country_code":"CA","type":"funder","lineage":["https://openalex.org/I4210133112"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Inanc Birol","raw_affiliation_strings":["British Columbia Cancer Agency, Canada's Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada"],"affiliations":[{"raw_affiliation_string":"British Columbia Cancer Agency, Canada's Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada","institution_ids":["https://openalex.org/I4210133112"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5055394860"],"corresponding_institution_ids":["https://openalex.org/I4210133112"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.11950661,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T11447","display_name":"Blind Source Separation Techniques","score":0.9950000047683716,"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/T10057","display_name":"Face and Expression Recognition","score":0.9943000078201294,"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/cluster-analysis","display_name":"Cluster analysis","score":0.8670698404312134},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.7142988443374634},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5960922241210938},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5376694202423096},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5204641819000244},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.507216215133667},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.46711668372154236},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4668809771537781},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.434524804353714},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4301387071609497},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38258761167526245},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.377201646566391},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.07525038719177246}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8670698404312134},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.7142988443374634},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5960922241210938},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5376694202423096},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5204641819000244},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.507216215133667},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.46711668372154236},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4668809771537781},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.434524804353714},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4301387071609497},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38258761167526245},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.377201646566391},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.07525038719177246},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","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/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727698","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727698","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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":23,"referenced_works":["https://openalex.org/W23725892","https://openalex.org/W137313310","https://openalex.org/W221787649","https://openalex.org/W1992487663","https://openalex.org/W1995255737","https://openalex.org/W2007037775","https://openalex.org/W2021231414","https://openalex.org/W2039934205","https://openalex.org/W2051549110","https://openalex.org/W2064080972","https://openalex.org/W2110335417","https://openalex.org/W2112545207","https://openalex.org/W2116810533","https://openalex.org/W2126337883","https://openalex.org/W2132914434","https://openalex.org/W2165874743","https://openalex.org/W2204293552","https://openalex.org/W2963098024","https://openalex.org/W4205648292","https://openalex.org/W6663082325","https://openalex.org/W6682281974","https://openalex.org/W6684578312","https://openalex.org/W6688143433"],"related_works":["https://openalex.org/W2389214306","https://openalex.org/W4235240664","https://openalex.org/W1838576100","https://openalex.org/W2965083567","https://openalex.org/W2095886385","https://openalex.org/W2089704382","https://openalex.org/W1983399550","https://openalex.org/W97075385","https://openalex.org/W2357523926","https://openalex.org/W2889616422"],"abstract_inverted_index":{"Spectral":[0],"clustering":[1,59,84,118],"has":[2],"shown":[3],"a":[4,81,87],"superior":[5],"performance":[6,102,111],"in":[7,20,55,112],"analyzing":[8,21],"the":[9,13,37,101,104,115],"cluster":[10],"structure.":[11],"However,":[12],"exponentially":[14],"computational":[15],"complexity":[16],"limits":[17],"its":[18,109],"application":[19],"large-scale":[22],"data.":[23],"To":[24],"tackle":[25],"this":[26],"problem,":[27],"many":[28],"low-rank":[29],"matrix":[30],"approximating":[31],"algorithms":[32,49,60],"are":[33],"proposed,":[34],"of":[35,73,90,100,103],"which":[36],"Nystr\u00f6m":[38,62,122],"method":[39],"is":[40],"an":[41],"approach":[42],"with":[43,86],"proved":[44],"lower":[45],"approximate":[46],"errors.":[47],"The":[48],"commonly":[50,64],"combine":[51],"two":[52],"powerful":[53],"techniques":[54],"machine":[56],"learning:":[57],"spectral":[58,117],"and":[61,107],"methods":[63,119],"used":[65],"to":[66,79,114],"obtain":[67],"good":[68],"quality":[69],"low":[70],"rank":[71],"approximations":[72],"large":[74],"matrices.":[75],"This":[76],"paper":[77],"proposes":[78],"analyze":[80],"scalable":[82],"Nystr\u00f6m-based":[83],"algorithm":[85,105],"Minimum":[88],"Sum":[89],"Squared":[91],"Similarities":[92],"(MSSS)":[93],"sampling":[94],"procedure.":[95],"We":[96],"provide":[97],"theoretical":[98,110],"analysis":[99],"MSSS":[106],"demonstrate":[108],"comparison":[113],"leading":[116],"that":[120],"use":[121],"sampling.":[123]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}