{"id":"https://openalex.org/W2980484302","doi":"https://doi.org/10.1109/access.2019.2947548","title":"Eigenspectrum Regularization on Grassmann Discriminant Analysis With Image Set Classification","display_name":"Eigenspectrum Regularization on Grassmann Discriminant Analysis With Image Set Classification","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2980484302","doi":"https://doi.org/10.1109/access.2019.2947548","mag":"2980484302"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2947548","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2947548","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2019.2947548","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088058763","display_name":"Hengliang Tan","orcid":"https://orcid.org/0000-0003-2167-156X"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hengliang Tan","raw_affiliation_strings":["School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2167-156X","affiliations":[{"raw_affiliation_string":"School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101948779","display_name":"Ying Gao","orcid":"https://orcid.org/0000-0002-2390-530X"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Gao","raw_affiliation_strings":["School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-2390-530X","affiliations":[{"raw_affiliation_string":"School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069957703","display_name":"Jiao Du","orcid":"https://orcid.org/0000-0001-6402-1335"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiao Du","raw_affiliation_strings":["School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101601665","display_name":"Shuo Yang","orcid":"https://orcid.org/0000-0003-4516-9953"},"institutions":[{"id":"https://openalex.org/I37987034","display_name":"Guangzhou University","ror":"https://ror.org/05ar8rn06","country_code":"CN","type":"education","lineage":["https://openalex.org/I37987034"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuo Yang","raw_affiliation_strings":["School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China","institution_ids":["https://openalex.org/I37987034"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I37987034"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.2032,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.55067078,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"7","issue":null,"first_page":"150792","last_page":"150804"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9986000061035156,"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":0.9986000061035156,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9833999872207642,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9186000227928162,"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/grassmannian","display_name":"Grassmannian","score":0.7449162602424622},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.6943024396896362},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5802677273750305},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.5634328722953796},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5329941511154175},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.521102786064148},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4956260919570923},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.48748135566711426},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4687172472476959},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.45613178610801697},{"id":"https://openalex.org/keywords/eigenface","display_name":"Eigenface","score":0.43543893098831177},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.4315439462661743},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.36952924728393555},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.30196255445480347},{"id":"https://openalex.org/keywords/combinatorics","display_name":"Combinatorics","score":0.21096530556678772},{"id":"https://openalex.org/keywords/pure-mathematics","display_name":"Pure mathematics","score":0.11598065495491028}],"concepts":[{"id":"https://openalex.org/C162929932","wikidata":"https://www.wikidata.org/wiki/Q129638","display_name":"Grassmannian","level":2,"score":0.7449162602424622},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.6943024396896362},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5802677273750305},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.5634328722953796},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5329941511154175},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.521102786064148},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4956260919570923},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.48748135566711426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4687172472476959},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.45613178610801697},{"id":"https://openalex.org/C104906051","wikidata":"https://www.wikidata.org/wiki/Q29695","display_name":"Eigenface","level":4,"score":0.43543893098831177},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.4315439462661743},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.36952924728393555},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30196255445480347},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.21096530556678772},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.11598065495491028}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2947548","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2947548","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8b01e6d0217a452daa26bb5d646bc2ff","is_oa":true,"landing_page_url":"https://doaj.org/article/8b01e6d0217a452daa26bb5d646bc2ff","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 150792-150804 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2947548","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2947548","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7200000286102295}],"awards":[{"id":"https://openalex.org/G3202162127","display_name":null,"funder_award_id":"61701126","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4880599153","display_name":null,"funder_award_id":"61802079","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8802950743","display_name":null,"funder_award_id":"61802148","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1503513760","https://openalex.org/W1556074820","https://openalex.org/W1820849028","https://openalex.org/W1851814560","https://openalex.org/W1929887928","https://openalex.org/W1964470356","https://openalex.org/W1985809919","https://openalex.org/W1994855553","https://openalex.org/W1996939238","https://openalex.org/W2000771160","https://openalex.org/W2010770092","https://openalex.org/W2021012145","https://openalex.org/W2025341678","https://openalex.org/W2031794871","https://openalex.org/W2045512849","https://openalex.org/W2052964740","https://openalex.org/W2066986622","https://openalex.org/W2084146405","https://openalex.org/W2084944189","https://openalex.org/W2088900896","https://openalex.org/W2091804476","https://openalex.org/W2112074816","https://openalex.org/W2117138194","https://openalex.org/W2118300173","https://openalex.org/W2121647436","https://openalex.org/W2123921160","https://openalex.org/W2124925761","https://openalex.org/W2126017757","https://openalex.org/W2134849720","https://openalex.org/W2137412091","https://openalex.org/W2141425367","https://openalex.org/W2142040002","https://openalex.org/W2143304877","https://openalex.org/W2144093206","https://openalex.org/W2148373517","https://openalex.org/W2149652297","https://openalex.org/W2150600350","https://openalex.org/W2156909104","https://openalex.org/W2160888808","https://openalex.org/W2162813111","https://openalex.org/W2165554381","https://openalex.org/W2171928062","https://openalex.org/W2512455028","https://openalex.org/W2741668914","https://openalex.org/W2798909945","https://openalex.org/W2962772276","https://openalex.org/W3097096317","https://openalex.org/W6630138755","https://openalex.org/W6677833576","https://openalex.org/W6678412491","https://openalex.org/W6680266532","https://openalex.org/W6680889708","https://openalex.org/W6680911227","https://openalex.org/W6684006261"],"related_works":["https://openalex.org/W2949616856","https://openalex.org/W1804640662","https://openalex.org/W2146523743","https://openalex.org/W2287336960","https://openalex.org/W2963346868","https://openalex.org/W4297634020","https://openalex.org/W112369303","https://openalex.org/W2057238507","https://openalex.org/W188689747","https://openalex.org/W2949472927"],"abstract_inverted_index":{"Set-based":[0],"image":[1,28],"classification":[2],"has":[3],"become":[4],"popular":[5],"in":[6,68,96,120],"recent":[7],"years":[8],"since":[9],"it":[10,126],"can":[11],"provide":[12],"a":[13,34,43],"relatively":[14],"large":[15],"amount":[16],"of":[17,65,73,79,87,118,159],"within-set":[18],"information":[19],"that":[20],"benefits":[21],"classification.":[22],"Grassmann":[23,35],"Discriminant":[24,53],"Analysis":[25,54],"(GDA)":[26],"models":[27,136],"sets":[29],"as":[30,76],"points":[31],"(subspaces)":[32],"on":[33,147],"manifold":[36],"and":[37,62,84,151],"then":[38],"explicitly":[39],"maps":[40],"them":[41],"to":[42,58,113],"higher-dimensional":[44],"Hilbert":[45],"space,":[46],"where":[47],"Euclidean":[48],"geometry":[49],"applies,":[50],"for":[51],"Linear":[52],"(LDA).":[55],"However,":[56],"due":[57],"the":[59,70,77,80,85,115,128,140,157],"noise":[60],"disturbance":[61],"finite":[63],"number":[64],"training":[66],"samples":[67],"practice,":[69],"conventional":[71,116],"problems":[72,117],"LDA,":[74],"such":[75],"singularity":[78],"within-class":[81],"scatter":[82],"matrix":[83],"instability":[86],"its":[88],"inverse,":[89],"also":[90],"appear":[91],"with":[92,127],"GDA,":[93],"which":[94],"result":[95],"recognition":[97,150],"performance":[98],"deterioration.":[99],"Inspired":[100],"by":[101],"eigenspectrum":[102,108,134],"regularization":[103,135],"techniques,":[104],"we":[105,124],"propose":[106],"an":[107],"Regularized":[109],"GDA":[110,119],"(RGDA)":[111],"method":[112],"alleviate":[114],"Grassmannian":[121],"space.":[122],"Moreover,":[123],"implement":[125],"graph":[129],"embedded":[130],"framework,":[131],"three":[132],"different":[133],"are":[137],"incorporated":[138],"into":[139],"proposed":[141],"approach":[142],"respectively.":[143],"Extensive":[144],"experimental":[145],"results":[146],"set-based":[148],"face":[149],"object":[152],"categorization":[153],"tasks":[154],"have":[155],"confirmed":[156],"effectiveness":[158],"our":[160],"approaches.":[161]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
