{"id":"https://openalex.org/W2011646906","doi":"https://doi.org/10.4304/jcp.9.7.1704-1711","title":"Evaluation of Distance Measures For NMF-Based Face Image Applications","display_name":"Evaluation of Distance Measures For NMF-Based Face Image Applications","publication_year":2014,"publication_date":"2014-06-23","ids":{"openalex":"https://openalex.org/W2011646906","doi":"https://doi.org/10.4304/jcp.9.7.1704-1711","mag":"2011646906"},"language":"en","primary_location":{"id":"doi:10.4304/jcp.9.7.1704-1711","is_oa":false,"landing_page_url":"https://doi.org/10.4304/jcp.9.7.1704-1711","pdf_url":null,"source":{"id":"https://openalex.org/S77894049","display_name":"Journal of Computers","issn_l":"1796-203X","issn":["1796-203X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318660","host_organization_name":"Academy Publisher","host_organization_lineage":["https://openalex.org/P4310318660"],"host_organization_lineage_names":["Academy Publisher"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computers","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/A5015564248","display_name":"Yun Xue","orcid":"https://orcid.org/0000-0002-4048-5298"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yun Xue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111837129","display_name":"Chong Sze Tong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chong Sze Tong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5030624242","display_name":"Tiechen Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tiechen Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.08562702,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"9","issue":"7","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":0.9994999766349792,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9954000115394592,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9815999865531921,"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/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.8587944507598877},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7865862846374512},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.689703106880188},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6870208382606506},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.5938949584960938},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.5479037165641785},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5340164303779602},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.5017547607421875},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4905382990837097},{"id":"https://openalex.org/keywords/basis","display_name":"Basis (linear algebra)","score":0.4903392791748047},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47599780559539795},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.38672885298728943},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.37738293409347534},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23229706287384033}],"concepts":[{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.8587944507598877},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7865862846374512},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.689703106880188},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6870208382606506},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.5938949584960938},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.5479037165641785},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5340164303779602},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.5017547607421875},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4905382990837097},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.4903392791748047},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47599780559539795},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.38672885298728943},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.37738293409347534},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23229706287384033},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"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/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4304/jcp.9.7.1704-1711","is_oa":false,"landing_page_url":"https://doi.org/10.4304/jcp.9.7.1704-1711","pdf_url":null,"source":{"id":"https://openalex.org/S77894049","display_name":"Journal of Computers","issn_l":"1796-203X","issn":["1796-203X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318660","host_organization_name":"Academy Publisher","host_organization_lineage":["https://openalex.org/P4310318660"],"host_organization_lineage_names":["Academy Publisher"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Computers","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1585192441","https://openalex.org/W1902027874","https://openalex.org/W2008107235","https://openalex.org/W2020959717","https://openalex.org/W2033419168","https://openalex.org/W2099111195","https://openalex.org/W2102760078","https://openalex.org/W2105237783","https://openalex.org/W2108301944","https://openalex.org/W2127877256","https://openalex.org/W2135029798","https://openalex.org/W2138451337","https://openalex.org/W2138729701","https://openalex.org/W2146342856","https://openalex.org/W2155199877","https://openalex.org/W2163624601","https://openalex.org/W2749035964","https://openalex.org/W3143596294","https://openalex.org/W6629369404","https://openalex.org/W6635115365","https://openalex.org/W6680012447","https://openalex.org/W6680499448"],"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/W2146544734"],"abstract_inverted_index":{"Non-negative":[0],"matrix":[1],"factorization":[2],"(NMF)":[3],"is":[4,13,27,73],"an":[5],"increasingly":[6],"popular":[7],"feature":[8,71],"extraction":[9],"method.":[10],"Since":[11],"it":[12,26,33],"designed":[14],"to":[15,53],"fit":[16],"training":[17],"samples":[18],"using":[19,120,154],"linear":[20],"combination":[21],"of":[22,67,88,143],"non-negative":[23,101],"basis":[24,45],"vectors,":[25],"particular":[28],"suitable":[29],"for":[30,57,82,110],"imageapplications":[31],"as":[32,98],"affords":[34],"intuitive":[35],"localized":[36],"interpretations.However,":[37],"in":[38,94,112],"this":[39,63],"space":[40],"defined":[41],"by":[42],"the":[43,65,77,89,121,141,151,155,159],"NMF":[44,80,144,152],"images,there":[46],"has":[47],"not":[48],"been":[49],"any":[50],"systematic":[51],"research":[52],"identifysuitable":[54],"distance":[55,69,91,157],"measure":[56],"NMF-based":[58,113],"data":[59],"classification.":[60],"\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0In":[62],"paper,":[64],"performance":[66,142],"19":[68],"measuresbetween":[70],"vectors":[72],"evaluated":[74],"based":[75],"on":[76],"result":[78],"ofthe":[79],"algorithm":[81,153],"face":[83,95],"recognition,":[84],"which":[85],"include":[86],"most":[87],"standard":[90],"measures":[92],"used":[93],"recognition,as":[96],"well":[97],"two":[99],"new":[100],"vector":[102],"similarity":[103],"coefficient-based":[104],"(NVSC)":[105],"distances":[106],"that":[107,150],"we":[108],"recommend":[109],"use":[111],"pattern":[114],"recognition.":[115],"Recognition":[116],"experiments":[117],"are":[118],"performed":[119],"CMU":[122],"AMP":[123],"Face":[124],"Expression":[125],"database,":[126,128,130,132],"CBCL2":[127],"MIT-CBCL":[129],"YaleB":[131],"and":[133,148],"FERET":[134],"database.":[135],"We":[138],"also":[139],"compared":[140],"with":[145],"Eigenface":[146],"method":[147],"showed":[149],"NVSC":[156],"yielded":[158],"best":[160],"recognition":[161],"results.":[162]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}