{"id":"https://openalex.org/W4398178330","doi":"https://doi.org/10.1145/3665500","title":"On Mean-Optimal Robust Linear Discriminant Analysis","display_name":"On Mean-Optimal Robust Linear Discriminant Analysis","publication_year":2024,"publication_date":"2024-05-21","ids":{"openalex":"https://openalex.org/W4398178330","doi":"https://doi.org/10.1145/3665500"},"language":"en","primary_location":{"id":"doi:10.1145/3665500","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3665500","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3665500","source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3665500","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001033117","display_name":"Xiangyu Li","orcid":"https://orcid.org/0000-0002-1480-3686"},"institutions":[{"id":"https://openalex.org/I167576493","display_name":"Colorado School of Mines","ror":"https://ror.org/04raf6v53","country_code":"US","type":"education","lineage":["https://openalex.org/I167576493"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiangyu Li","raw_affiliation_strings":["Colorado School of Mines, Golden, CO, USA"],"raw_orcid":"https://orcid.org/0000-0002-1480-3686","affiliations":[{"raw_affiliation_string":"Colorado School of Mines, Golden, CO, USA","institution_ids":["https://openalex.org/I167576493"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101849466","display_name":"Hua Wang","orcid":"https://orcid.org/0000-0002-5986-7413"},"institutions":[{"id":"https://openalex.org/I167576493","display_name":"Colorado School of Mines","ror":"https://ror.org/04raf6v53","country_code":"US","type":"education","lineage":["https://openalex.org/I167576493"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hua Wang","raw_affiliation_strings":["Colorado School of Mines, Golden, CO, USA"],"raw_orcid":"https://orcid.org/0000-0002-5986-7413","affiliations":[{"raw_affiliation_string":"Colorado School of Mines, Golden, CO, USA","institution_ids":["https://openalex.org/I167576493"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I167576493"],"apc_list":null,"apc_paid":null,"fwci":0.3003,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.48305462,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"18","issue":"8","first_page":"1","last_page":"27"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9994999766349792,"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/T10057","display_name":"Face and Expression Recognition","score":0.9991999864578247,"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/T12676","display_name":"Machine Learning and ELM","score":0.9873999953269958,"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/mathematical-optimization","display_name":"Mathematical optimization","score":0.5909781455993652},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.5659112334251404},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5298582911491394},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5193289518356323},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.4692862927913666},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.43793806433677673},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4011121988296509},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26856565475463867}],"concepts":[{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5909781455993652},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.5659112334251404},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5298582911491394},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5193289518356323},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.4692862927913666},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.43793806433677673},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4011121988296509},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26856565475463867}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3665500","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3665500","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3665500","source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3665500","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3665500","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3665500","source":{"id":"https://openalex.org/S41523882","display_name":"ACM Transactions on Knowledge Discovery from Data","issn_l":"1556-4681","issn":["1556-4681","1556-472X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Knowledge Discovery from Data","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.75,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4398178330.pdf"},"referenced_works_count":45,"referenced_works":["https://openalex.org/W147992333","https://openalex.org/W191001584","https://openalex.org/W1512226162","https://openalex.org/W1997011019","https://openalex.org/W2008277111","https://openalex.org/W2027982384","https://openalex.org/W2057069782","https://openalex.org/W2088616581","https://openalex.org/W2100495367","https://openalex.org/W2100659887","https://openalex.org/W2112796928","https://openalex.org/W2131777119","https://openalex.org/W2135779729","https://openalex.org/W2145094598","https://openalex.org/W2145962650","https://openalex.org/W2158281641","https://openalex.org/W2166693468","https://openalex.org/W2257217404","https://openalex.org/W2296157260","https://openalex.org/W2598654328","https://openalex.org/W2603766943","https://openalex.org/W2791052411","https://openalex.org/W2798273810","https://openalex.org/W2807548733","https://openalex.org/W2901373116","https://openalex.org/W2955863859","https://openalex.org/W2982772166","https://openalex.org/W2997574889","https://openalex.org/W3004545648","https://openalex.org/W3011249019","https://openalex.org/W3161106602","https://openalex.org/W3203040596","https://openalex.org/W3205506291","https://openalex.org/W4226097392","https://openalex.org/W4226450359","https://openalex.org/W4230343247","https://openalex.org/W4249736682","https://openalex.org/W4285166886","https://openalex.org/W4298379700","https://openalex.org/W4318823521","https://openalex.org/W4387717652","https://openalex.org/W4390017890","https://openalex.org/W4391528877","https://openalex.org/W6787267479","https://openalex.org/W6839498709"],"related_works":["https://openalex.org/W2114217318","https://openalex.org/W2794812819","https://openalex.org/W2587881214","https://openalex.org/W3104072235","https://openalex.org/W3036945320","https://openalex.org/W2370263288","https://openalex.org/W2169311637","https://openalex.org/W3192451249","https://openalex.org/W4367850163","https://openalex.org/W2395040056"],"abstract_inverted_index":{"Linear":[0],"discriminant":[1],"analysis":[2],"(LDA)":[3],"is":[4,54,70,84,87,109],"widely":[5],"used":[6],"for":[7,133],"dimensionality":[8],"reduction":[9],"under":[10],"supervised":[11],"learning":[12],"settings.":[13],"Traditional":[14],"LDA":[15,37,96],"objective":[16,75,132,145,174,187],"aims":[17],"to":[18,42,65,92,136,150,167,193,219],"minimize":[19],"the":[20,23,60,67,74,107,138,152,169,186,189,209,220],"ratio":[21],"of":[22,204,211],"squared":[24,61],"Euclidean":[25],"distances":[26],"that":[27,55,85,106,179,184],"may":[28],"not":[29,71],"perform":[30],"optimally":[31],"on":[32,77],"noisy":[33],"datasets.":[34],"Multiple":[35],"robust":[36,95,122,131],"objectives":[38],"have":[39,49],"been":[40],"proposed":[41],"address":[43],"this":[44,117],"problem,":[45],"but":[46],"their":[47,56],"implementations":[48],"two":[50],"major":[51],"limitations.":[52],"One":[53],"mean":[57,139],"calculations":[58],"use":[59],"\\(\\ell_{2}\\)":[62],"-norm":[63],"distance":[64,79],"center":[66,151],"data,":[68],"which":[69],"valid":[72],"when":[73],"depends":[76],"other":[78,221],"functions.":[80],"The":[81,202],"second":[82],"problem":[83],"there":[86],"no":[88],"generalized":[89,130],"optimization":[90],"algorithm":[91,166,182],"solve":[93],"different":[94],"objectives.":[97],"In":[98,116],"addition,":[99],"most":[100],"existing":[101],"algorithms":[102],"can":[103],"only":[104],"guarantee":[105],"solution":[108,181,190],"locally":[110],"optimal":[111,148,195],"rather":[112],"than":[113],"globally":[114,194],"optimal.":[115],"article,":[118],"we":[119,161],"review":[120],"multiple":[121],"loss":[123],"functions":[124],"and":[125,129,172,188],"propose":[126],"a":[127,198],"new":[128,213],"LDA.":[134],"Besides,":[135],"remove":[137],"value":[140],"within":[141],"data":[142,153],"better,":[143],"our":[144,180,212],"uses":[146],"an":[147,163],"way":[149],"through":[154],"learning.":[155],"As":[156],"one":[157],"important":[158],"algorithmic":[159],"contribution,":[160],"derive":[162],"efficient":[164],"iterative":[165],"optimize":[168],"resulting":[170],"non-smooth":[171],"non-convex":[173],"function.":[175],"We":[176],"theoretically":[177],"prove":[178],"guarantees":[183],"both":[185],"sequences":[191],"converge":[192],"solutions":[196],"at":[197],"sub-linear":[199],"convergence":[200],"rate.":[201],"results":[203],"comprehensive":[205],"experimental":[206],"evaluations":[207],"demonstrate":[208],"effectiveness":[210],"method,":[214],"achieving":[215],"significant":[216],"improvements":[217],"compared":[218],"competing":[222],"methods.":[223]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
