{"id":"https://openalex.org/W1654763638","doi":"https://doi.org/10.1109/ijcnn.2015.7280465","title":"Spectral clustering of high-dimensional data via Nonnegative Matrix Factorization","display_name":"Spectral clustering of high-dimensional data via Nonnegative Matrix Factorization","publication_year":2015,"publication_date":"2015-07-01","ids":{"openalex":"https://openalex.org/W1654763638","doi":"https://doi.org/10.1109/ijcnn.2015.7280465","mag":"1654763638"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2015.7280465","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2015.7280465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 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/A5101485863","display_name":"Shulin Wang","orcid":"https://orcid.org/0000-0003-1474-6455"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shulin Wang","raw_affiliation_strings":["College of Computer Science & Electronics Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science & Electronics Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100766597","display_name":"Fang Chen","orcid":"https://orcid.org/0000-0002-4410-7040"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fang Chen","raw_affiliation_strings":["College of Computer Science & Electronics Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science & Electronics Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025755039","display_name":"Jianwen Fang","orcid":"https://orcid.org/0000-0002-6502-6004"},"institutions":[{"id":"https://openalex.org/I4210140884","display_name":"National Cancer Institute","ror":"https://ror.org/040gcmg81","country_code":"US","type":"government","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210140884"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianwen Fang","raw_affiliation_strings":["Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, USA"],"affiliations":[{"raw_affiliation_string":"Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, USA","institution_ids":["https://openalex.org/I4210140884"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101485863"],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":0.7364,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.78084486,"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":"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":0.9987999796867371,"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":0.9987999796867371,"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/T10885","display_name":"Gene expression and cancer classification","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9866999983787537,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.7351645231246948},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.7306839227676392},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.5645826458930969},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5522183179855347},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5350877642631531},{"id":"https://openalex.org/keywords/matrix","display_name":"Matrix (chemical analysis)","score":0.4994974136352539},{"id":"https://openalex.org/keywords/matrix-algebra","display_name":"Matrix algebra","score":0.4609464108943939},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.4454885423183441},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3533087968826294},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33346423506736755},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32381972670555115},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.228743314743042},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.17487174272537231},{"id":"https://openalex.org/keywords/eigenvalues-and-eigenvectors","display_name":"Eigenvalues and eigenvectors","score":0.09656625986099243}],"concepts":[{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.7351645231246948},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.7306839227676392},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.5645826458930969},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5522183179855347},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5350877642631531},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.4994974136352539},{"id":"https://openalex.org/C2988995629","wikidata":"https://www.wikidata.org/wiki/Q2915729","display_name":"Matrix algebra","level":3,"score":0.4609464108943939},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.4454885423183441},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3533087968826294},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33346423506736755},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32381972670555115},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.228743314743042},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.17487174272537231},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.09656625986099243},{"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/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2015.7280465","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2015.7280465","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W52853789","https://openalex.org/W113657110","https://openalex.org/W1551136530","https://openalex.org/W1727290854","https://openalex.org/W1902027874","https://openalex.org/W1984110133","https://openalex.org/W1993137812","https://openalex.org/W1993962865","https://openalex.org/W1997201895","https://openalex.org/W2003217181","https://openalex.org/W2007266727","https://openalex.org/W2032223996","https://openalex.org/W2044512962","https://openalex.org/W2045727533","https://openalex.org/W2050385561","https://openalex.org/W2060371459","https://openalex.org/W2064208261","https://openalex.org/W2069959554","https://openalex.org/W2087684630","https://openalex.org/W2109363337","https://openalex.org/W2121947440","https://openalex.org/W2124704116","https://openalex.org/W2125531986","https://openalex.org/W2126375903","https://openalex.org/W2132914434","https://openalex.org/W2135029798","https://openalex.org/W2138218344","https://openalex.org/W2165874743","https://openalex.org/W2169658215","https://openalex.org/W3143596294","https://openalex.org/W4246697467","https://openalex.org/W6632976901","https://openalex.org/W6680012447","https://openalex.org/W6684578312","https://openalex.org/W6822831480"],"related_works":["https://openalex.org/W2127243424","https://openalex.org/W4390394189","https://openalex.org/W2037504162","https://openalex.org/W2539013788","https://openalex.org/W2792706544","https://openalex.org/W1568451138","https://openalex.org/W2156699640","https://openalex.org/W2045265907","https://openalex.org/W2972997031","https://openalex.org/W2075222291"],"abstract_inverted_index":{"Spectral":[0],"clustering":[1,7,44,48,82,89,94,147,160,181,250],"has":[2,84],"become":[3],"a":[4,19,27,32,36,69,73,157],"popular":[5],"subspace":[6],"algorithm":[8,161,176],"in":[9,96,146,249],"machine":[10],"learning":[11],"and":[12,38,61,90,197,243],"data":[13,54,65],"mining,":[14],"which":[15,130,162],"aims":[16],"at":[17],"finding":[18],"low-dimensional":[20],"representation":[21,79,106,241],"by":[22,103],"utilizing":[23],"the":[24,51,116,119,126,133,192,195,207,234],"spectrum":[25],"of":[26,59,64,118,128,185,194,202,236],"Laplacian":[28],"matrix.":[29,76],"It":[30],"is":[31,68,111,123,141,189,247],"key":[33],"to":[34,45,71,92,125,143],"construct":[35,72],"discriminative":[37,137],"reliable":[39],"affinity":[40,75,101,134,164,208],"matrix":[41,102,135,165,209],"for":[42,88],"spectral":[43,81,159,180],"achieve":[46],"impressive":[47],"quality.":[49],"As":[50],"real":[52],"word":[53],"increase":[55],"with":[56,217],"higher":[57],"dimension":[58,117,184],"features":[60],"larger":[62],"number":[63,127,193],"samples,":[66,129],"it":[67,110,140],"challenge":[70],"good":[74],"Recently,":[77],"sparse":[78,105,120,203,240],"based":[80,179],"(SRSC)":[83],"proven":[85],"its":[86],"efficiency":[87],"lead":[91],"promising":[93,248],"results":[95,224],"high-dimensional":[97,251],"data.":[98,252],"SRSC":[99,145],"constructs":[100,163],"using":[104],"coefficient":[107,121,171,187,204,215,238,242],"vectors.":[108,172],"However,":[109],"very":[112],"time":[113],"consuming.":[114],"Additionally,":[115],"vector":[122,188,216],"equal":[124],"may":[131],"make":[132],"not":[136],"enough.":[138],"Therefore,":[139,206],"inefficient":[142],"apply":[144],"large":[148],"scale":[149],"datasets.":[150],"To":[151],"remedy":[152],"these":[153],"issues,":[154],"we":[155],"propose":[156],"new":[158],"via":[166,213],"Nonnegative":[167],"Matrix":[168],"Factorization":[169],"(NMF)":[170],"We":[173],"call":[174],"our":[175],"as":[177],"NMF":[178,186,214,237],"(NMFSC).":[182],"The":[183,222],"independent":[190],"on":[191,225],"samples":[196],"significantly":[198],"smaller":[199],"than":[200],"that":[201,245],"vector.":[205],"can":[210],"be":[211],"constructed":[212],"much":[218],"lower":[219],"computational":[220],"cost.":[221],"experimental":[223],"several":[226],"public":[227],"gene":[228],"expression":[229],"profiling":[230],"(GEP)":[231],"datasets":[232],"demonstrate":[233],"advantage":[235],"over":[239],"suggest":[244],"NMFSC":[246]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}