{"id":"https://openalex.org/W2292995944","doi":"https://doi.org/10.1109/icip.2015.7350871","title":"Pareto-optimal discriminant analysis","display_name":"Pareto-optimal discriminant analysis","publication_year":2015,"publication_date":"2015-09-01","ids":{"openalex":"https://openalex.org/W2292995944","doi":"https://doi.org/10.1109/icip.2015.7350871","mag":"2292995944"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2015.7350871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7350871","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Image Processing (ICIP)","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/A5034751153","display_name":"Felix Juefei-Xu","orcid":"https://orcid.org/0000-0002-0857-8611"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Felix Juefei-Xu","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, Pennsylvania, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057959136","display_name":"Marios Savvides","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marios Savvides","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, Pennsylvania, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, Pennsylvania, USA","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5034751153"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":1.841,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.9062343,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"27","issue":null,"first_page":"611","last_page":"615"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9995999932289124,"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":0.9995999932289124,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.996999979019165,"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"}},{"id":"https://openalex.org/T11447","display_name":"Blind Source Separation Techniques","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.7649497389793396},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.7014908790588379},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6162046194076538},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5829402208328247},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5654698014259338},{"id":"https://openalex.org/keywords/facial-recognition-system","display_name":"Facial recognition system","score":0.56447434425354},{"id":"https://openalex.org/keywords/discriminant","display_name":"Discriminant","score":0.5092159509658813},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.4821881353855133},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4736191928386688},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4254035949707031},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.41546499729156494},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38874515891075134},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.35097795724868774}],"concepts":[{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.7649497389793396},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.7014908790588379},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6162046194076538},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5829402208328247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5654698014259338},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.56447434425354},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.5092159509658813},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.4821881353855133},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4736191928386688},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4254035949707031},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.41546499729156494},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38874515891075134},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35097795724868774},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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:10.1109/icip.2015.7350871","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2015.7350871","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W817302918","https://openalex.org/W1836618934","https://openalex.org/W1902324625","https://openalex.org/W1913277147","https://openalex.org/W1954390275","https://openalex.org/W1975346140","https://openalex.org/W2000781128","https://openalex.org/W2001141328","https://openalex.org/W2024808535","https://openalex.org/W2046873811","https://openalex.org/W2053186076","https://openalex.org/W2084481049","https://openalex.org/W2085388325","https://openalex.org/W2092545044","https://openalex.org/W2098693229","https://openalex.org/W2121647436","https://openalex.org/W2123921160","https://openalex.org/W2125874614","https://openalex.org/W2128659236","https://openalex.org/W2132549764","https://openalex.org/W2137659841","https://openalex.org/W2147514478","https://openalex.org/W2154872931","https://openalex.org/W2156718197","https://openalex.org/W2172156772","https://openalex.org/W2293213573","https://openalex.org/W2294869454","https://openalex.org/W2295428427","https://openalex.org/W2396243083","https://openalex.org/W6682644385","https://openalex.org/W6682755970"],"related_works":["https://openalex.org/W3100286349","https://openalex.org/W2896134808","https://openalex.org/W3172436493","https://openalex.org/W4287164812","https://openalex.org/W2957492749","https://openalex.org/W1887135636","https://openalex.org/W2386063599","https://openalex.org/W1975884855","https://openalex.org/W3213150849","https://openalex.org/W4285605394"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3,47],"have":[4,89],"proposed":[5,68,107],"the":[6,35,61,75,92],"Pareto-optimal":[7],"discriminant":[8,65],"analysis":[9],"(PDA),":[10],"an":[11,42,80],"optimally":[12],"designed":[13],"linear":[14,53,76,112],"subspace":[15,54,113],"learning":[16],"method":[17,69],"that":[18,105],"harnesses":[19],"advantages":[20],"across":[21],"many":[22],"well-known":[23],"methods":[24],"such":[25],"as":[26],"PCA,":[27],"LDA,":[28],"UDP":[29],"and":[30,39,100],"LPP.":[31],"By":[32],"optimizing":[33],"over":[34],"joint":[36],"objective":[37,62],"function":[38,63],"carrying":[40],"out":[41],"alternative":[43],"coefficients":[44],"updating":[45],"scheme,":[46],"are":[48],"able":[49],"to":[50,58],"obtain":[51],"a":[52,85],"which":[55],"is":[56],"optimized":[57],"truly":[59],"maximize":[60],"in":[64,79,91],"analysis.":[66],"The":[67],"also":[70],"provides":[71],"flexibility":[72],"for":[73,84],"formulating":[74],"transformation":[77],"matrix":[78],"overcomplete":[81],"fashion,":[82],"allowing":[83],"sparse":[86],"representation.":[87],"We":[88],"shown,":[90],"context":[93],"of":[94],"large":[95],"scale":[96],"unconstrained":[97],"face":[98,103],"recognition":[99],"illumination":[101],"invariant":[102],"recognition,":[104],"our":[106],"PDA":[108],"significantly":[109],"outperforms":[110],"other":[111],"methods.":[114]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
