{"id":"https://openalex.org/W2040821848","doi":"https://doi.org/10.1109/lsp.2010.2093600","title":"Nearest Feature Space Analysis for Classification","display_name":"Nearest Feature Space Analysis for Classification","publication_year":2010,"publication_date":"2010-11-23","ids":{"openalex":"https://openalex.org/W2040821848","doi":"https://doi.org/10.1109/lsp.2010.2093600","mag":"2040821848"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2010.2093600","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2010.2093600","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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/A5100460385","display_name":"Jiwen Lu","orcid":"https://orcid.org/0000-0002-6121-5529"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Jiwen Lu","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, , Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, , Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103379503","display_name":"Yap\u2010Peng Tan","orcid":"https://orcid.org/0000-0002-0645-9109"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Yap-Peng Tan","raw_affiliation_strings":["School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, , Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]},{"raw_affiliation_string":"Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, , Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":4.7605,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.9544507,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"18","issue":"1","first_page":"55","last_page":"58"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9994999766349792,"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":0.9994999766349792,"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/T13717","display_name":"Advanced Algorithms and Applications","score":0.9605000019073486,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9569000005722046,"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/subspace-topology","display_name":"Subspace topology","score":0.8780856132507324},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.8139899969100952},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.7556833028793335},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6782680749893188},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.6722335815429688},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6672821044921875},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.657843291759491},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.649968147277832},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6182522773742676},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.535051703453064},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4707259237766266},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.46906280517578125},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.4667506515979767},{"id":"https://openalex.org/keywords/large-margin-nearest-neighbor","display_name":"Large margin nearest neighbor","score":0.46251073479652405},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.4371924102306366},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38643890619277954}],"concepts":[{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.8780856132507324},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.8139899969100952},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.7556833028793335},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6782680749893188},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.6722335815429688},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6672821044921875},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.657843291759491},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.649968147277832},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6182522773742676},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.535051703453064},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4707259237766266},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.46906280517578125},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4667506515979767},{"id":"https://openalex.org/C94475309","wikidata":"https://www.wikidata.org/wiki/Q6489154","display_name":"Large margin nearest neighbor","level":3,"score":0.46251073479652405},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.4371924102306366},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38643890619277954},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2010.2093600","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2010.2093600","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2053186076","https://openalex.org/W2100281586","https://openalex.org/W2117553576","https://openalex.org/W2121647436","https://openalex.org/W2125127226","https://openalex.org/W2138451337","https://openalex.org/W2143071151","https://openalex.org/W2146076056","https://openalex.org/W2159548580","https://openalex.org/W2163230565","https://openalex.org/W2994340921","https://openalex.org/W3148981562","https://openalex.org/W6635552349"],"related_works":["https://openalex.org/W2015098463","https://openalex.org/W2015442739","https://openalex.org/W2351157934","https://openalex.org/W2519241726","https://openalex.org/W2368666353","https://openalex.org/W4322721277","https://openalex.org/W2108882154","https://openalex.org/W2107754327","https://openalex.org/W2802487487","https://openalex.org/W2354127086"],"abstract_inverted_index":{"We":[0],"propose":[1,77,110],"in":[2,34,130],"this":[3,74],"letter":[4],"two":[5],"new":[6,112],"subspace":[7,28,50,89,97],"learning":[8,29],"methods,":[9],"called":[10],"nearest":[11,18,43,80],"feature":[12,19,81,119],"space":[13,20,82,120],"analysis":[14,21],"(NFSA)":[15],"and":[16,51,123,138],"discriminant":[17],"(DNFSA),":[22],"for":[23,98],"pattern":[24],"classification.":[25,99],"While":[26],"many":[27],"algorithms":[30],"have":[31],"been":[32],"proposed":[33,150],"recent":[35],"years,":[36],"most":[37],"of":[38,59,66,95,106,148],"them":[39],"apply":[40],"the":[41,49,56,60,64,79,92,96,103,117,125,131,146,149],"conventional":[42],"neighbor":[44],"(NN)":[45],"metric":[46,84],"to":[47,85,90,115,144],"derive":[48],"may":[52],"not":[53],"effectively":[54],"characterize":[55],"geometrical":[57],"information":[58],"samples,":[61],"especially":[62],"when":[63],"number":[65],"training":[67],"samples":[68],"per":[69],"class":[70],"is":[71],"limited.":[72],"In":[73],"paper,":[75],"we":[76,108],"using":[78],"(NFS)":[83],"seek":[86],"a":[87,111],"NFSA":[88],"improve":[91],"discriminating":[93],"power":[94,105],"To":[100],"further":[101],"enhance":[102],"discriminative":[104],"NFSA,":[107],"also":[109],"DNFSA":[113],"method":[114],"minimize":[116],"within-class":[118],"(FS)":[121],"distances":[122,128],"maximize":[124],"between-class":[126],"FS":[127],"simultaneously":[129],"derived":[132],"subspace.":[133],"Experimental":[134],"results":[135],"on":[136],"face":[137],"facial":[139],"expression":[140],"recognition":[141],"are":[142],"presented":[143],"demonstrate":[145],"efficacy":[147],"methods.":[151]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":4},{"year":2013,"cited_by_count":8},{"year":2012,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
