{"id":"https://openalex.org/W3089607999","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207089","title":"Hubness-based Sampling Method for Nystr\u00f6m Spectral Clustering","display_name":"Hubness-based Sampling Method for Nystr\u00f6m Spectral Clustering","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3089607999","doi":"https://doi.org/10.1109/ijcnn48605.2020.9207089","mag":"3089607999"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9207089","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 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/A5100700817","display_name":"Hongmin Li","orcid":"https://orcid.org/0009-0009-7380-0029"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Hongmin Li","raw_affiliation_strings":["Department of Computer Science, University of Tsukuba, Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Tsukuba, Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063256241","display_name":"Xiucai Ye","orcid":"https://orcid.org/0000-0002-5547-3919"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xiucai Ye","raw_affiliation_strings":["Department of Computer Science, University of Tsukuba, Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Tsukuba, Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047435295","display_name":"Akira Imakura","orcid":null},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akira Imakura","raw_affiliation_strings":["Department of Computer Science, University of Tsukuba, Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Tsukuba, Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038336830","display_name":"Tetsuya Sakurai","orcid":"https://orcid.org/0000-0002-5789-7547"},"institutions":[{"id":"https://openalex.org/I146399215","display_name":"University of Tsukuba","ror":"https://ror.org/02956yf07","country_code":"JP","type":"education","lineage":["https://openalex.org/I146399215"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tetsuya Sakurai","raw_affiliation_strings":["Department of Computer Science, University of Tsukuba, Tsukuba, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Tsukuba, Tsukuba, Japan","institution_ids":["https://openalex.org/I146399215"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100700817"],"corresponding_institution_ids":["https://openalex.org/I146399215"],"apc_list":null,"apc_paid":null,"fwci":0.3925,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62465717,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9975000023841858,"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":0.9975000023841858,"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9921000003814697,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9914000034332275,"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/cluster-analysis","display_name":"Cluster analysis","score":0.823255181312561},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.6313557028770447},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5593615174293518},{"id":"https://openalex.org/keywords/data-point","display_name":"Data point","score":0.4828634560108185},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.47579988837242126},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.46261411905288696},{"id":"https://openalex.org/keywords/nystr\u00f6m-method","display_name":"Nystr\u00f6m method","score":0.4552208483219147},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4297051429748535},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41126951575279236},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.387692391872406},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.37269851565361023},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3547244369983673},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.06051698327064514}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.823255181312561},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.6313557028770447},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5593615174293518},{"id":"https://openalex.org/C21080849","wikidata":"https://www.wikidata.org/wiki/Q13611879","display_name":"Data point","level":2,"score":0.4828634560108185},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.47579988837242126},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.46261411905288696},{"id":"https://openalex.org/C48265008","wikidata":"https://www.wikidata.org/wiki/Q7071295","display_name":"Nystr\u00f6m method","level":3,"score":0.4552208483219147},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4297051429748535},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41126951575279236},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.387692391872406},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37269851565361023},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3547244369983673},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.06051698327064514},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","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},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C27016315","wikidata":"https://www.wikidata.org/wiki/Q580101","display_name":"Integral equation","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9207089","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9207089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W297461772","https://openalex.org/W1554944419","https://openalex.org/W1967934524","https://openalex.org/W2013029404","https://openalex.org/W2021404082","https://openalex.org/W2095858069","https://openalex.org/W2112104211","https://openalex.org/W2116810533","https://openalex.org/W2126337883","https://openalex.org/W2127017215","https://openalex.org/W2132914434","https://openalex.org/W2134370969","https://openalex.org/W2141566892","https://openalex.org/W2157133710","https://openalex.org/W2165874743","https://openalex.org/W2171033594","https://openalex.org/W2342032814","https://openalex.org/W2346765529","https://openalex.org/W2487974819","https://openalex.org/W2810432309","https://openalex.org/W2903738625","https://openalex.org/W2962877334","https://openalex.org/W2963923734","https://openalex.org/W3010805239","https://openalex.org/W4247777826","https://openalex.org/W6610472804","https://openalex.org/W6633435507","https://openalex.org/W6653781006","https://openalex.org/W6684578312"],"related_works":["https://openalex.org/W4298130764","https://openalex.org/W2804364458","https://openalex.org/W2132641928","https://openalex.org/W4310225030","https://openalex.org/W2090259340","https://openalex.org/W1926736923","https://openalex.org/W2158836806","https://openalex.org/W2393816671","https://openalex.org/W1482912984","https://openalex.org/W2759831793"],"abstract_inverted_index":{"Nystr\u00f6m":[0,20,46,179],"method":[1,58,126,166],"is":[2,17,34,127],"widely":[3],"used":[4],"for":[5,36,178],"spectral":[6,48,181],"clustering":[7,134],"to":[8,19,90,129,147],"obtain":[9],"low-rank":[10],"approximations":[11],"of":[12,45,64,83,99,103],"a":[13,55],"large":[14],"matrix.":[15],"Sampling":[16],"crucial":[18],"method,":[21],"since":[22],"selecting":[23],"the":[24,31,43,61,71,79,94,100,112,124,150,164],"representative":[25],"sample":[26,65,95],"points":[27,69,107,115],"that":[28,163],"can":[29],"reflect":[30],"data":[32,68,85,106,121,160],"structure":[33],"important":[35],"obtaining":[37],"good":[38,170],"approximation":[39],"results.":[40,135],"To":[41],"improve":[42],"performance":[44],"based":[47,180],"clustering,":[49],"in":[50,78],"this":[51],"paper,":[52],"we":[53],"propose":[54],"new":[56],"sampling":[57,114,151,176],"by":[59],"considering":[60],"hubness":[62,73,110,144],"score":[63],"points.":[66,96],"The":[67],"with":[70,108,119],"high":[72,88,109],"scores,":[74],"i.e.,":[75,142],"appearing":[76],"frequently":[77],"nearest":[80],"neighbor":[81],"lists":[82],"other":[84,120,175],"points,":[86,122],"have":[87,116],"probabilities":[89],"be":[91],"selected":[92,113],"as":[93],"Taking":[97],"advantage":[98],"topological":[101],"property":[102],"hubs":[104],"(i.e.,":[105],"score),":[111],"close":[117],"relationships":[118],"thus":[123],"proposed":[125,165],"able":[128],"achieve":[130],"scalable":[131],"and":[132,158],"accurate":[133],"We":[136],"further":[137],"design":[138],"fast":[139],"computation":[140],"methods,":[141,146],"local":[143],"approximated":[145],"speed":[148],"up":[149],"process.":[152],"Experimental":[153],"results":[154],"on":[155],"both":[156],"synthetic":[157],"real-world":[159],"sets":[161],"show":[162],"not":[167],"only":[168],"achieves":[169],"performance,":[171],"but":[172],"also":[173],"outperforms":[174],"methods":[177],"clustering.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}
