{"id":"https://openalex.org/W7151552792","doi":"https://doi.org/10.48550/arxiv.2604.03928","title":"Supervised Dimensionality Reduction Revisited: Why LDA on Frozen CNN Features Deserves a Second Look","display_name":"Supervised Dimensionality Reduction Revisited: Why LDA on Frozen CNN Features Deserves a Second Look","publication_year":2026,"publication_date":"2026-04-05","ids":{"openalex":"https://openalex.org/W7151552792","doi":"https://doi.org/10.48550/arxiv.2604.03928"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03928","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03928","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.03928","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109534834","display_name":"Indar Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Indar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133079413","display_name":"Girish Karhana","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Karhana, Girish","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133109047","display_name":"Sai Krishna Jasti","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jasti, Sai Krishna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133104694","display_name":"Ankit Hemant Lade","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lade, Ankit Hemant","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11942","display_name":"Transportation and Mobility Innovations","score":0.714900016784668,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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/T11942","display_name":"Transportation and Mobility Innovations","score":0.714900016784668,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.19660000503063202,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T10698","display_name":"Transportation Planning and Optimization","score":0.019500000402331352,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5148000121116638},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5127999782562256},{"id":"https://openalex.org/keywords/variance-reduction","display_name":"Variance reduction","score":0.477400004863739},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.45579999685287476},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.4406000077724457},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.44020000100135803},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4377000033855438},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.40459999442100525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5735999941825867},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5148000121116638},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5127999782562256},{"id":"https://openalex.org/C62644790","wikidata":"https://www.wikidata.org/wiki/Q3454689","display_name":"Variance reduction","level":3,"score":0.477400004863739},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4722000062465668},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.45579999685287476},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.4406000077724457},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.44020000100135803},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.40459999442100525},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.38690000772476196},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.38670000433921814},{"id":"https://openalex.org/C2779206190","wikidata":"https://www.wikidata.org/wiki/Q162455","display_name":"Gini coefficient","level":4,"score":0.35679998993873596},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3476000130176544},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3118000030517578},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2809000015258789},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2703999876976013},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03928","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03928","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.03928","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03928","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Frozen":[0],"pretrained":[1],"image":[2,236],"representations":[3],"are":[4,17],"widely":[5],"used":[6],"for":[7,167,202,223],"transfer":[8],"learning:":[9],"a":[10,20,53,94,141],"backbone":[11],"is":[12,23,146,151],"kept":[13],"fixed,":[14],"feature":[15,33,119],"vectors":[16],"extracted,":[18],"and":[19,84,91,186,229],"lightweight":[21,188],"classifier":[22],"trained":[24],"on":[25,72,128],"top.":[26],"This":[27,139],"pipeline":[28],"usually":[29],"feeds":[30],"the":[31,36,40,48,198,218],"full":[32,102,132],"vector":[34],"to":[35,113,154],"classifier,":[37],"even":[38],"when":[39,148,224],"target":[41],"task":[42],"has":[43],"far":[44],"fewer":[45],"classes":[46],"than":[47],"pretraining":[49],"task.":[50],"We":[51,67,170],"revisit":[52],"classical":[54],"alternative:":[55],"supervised":[56,209,226],"dimensionality":[57,120],"reduction":[58,210],"with":[59,110,174],"Linear":[60],"Discriminant":[61,181],"Analysis":[62],"(LDA)":[63],"before":[64],"linear":[65],"probing.":[66],"evaluate":[68],"ten":[69],"dimensionality-reduction":[70],"strategies":[71],"frozen":[73],"features":[74,103,133],"from":[75],"six":[76,137],"backbones":[77],"--":[78,86],"ResNet-18,":[79],"ResNet-50,":[80],"MobileNetV3-Small,":[81],"EfficientNet-B0,":[82],"ViT-B/16,":[83],"DINOv2-ViT-S/14":[85],"across":[87,135],"CIFAR-100,":[88],"Tiny":[89],"ImageNet,":[90],"CUB-200-2011.":[92],"Under":[93],"fixed":[95],"logistic-regression":[96],"protocol,":[97],"LDA":[98,145,173,189,196],"improves":[99],"accuracy":[100],"over":[101],"in":[104],"11":[105],"of":[106],"12":[107],"coarse-grained":[108,204],"configurations,":[109],"gains":[111],"up":[112],"4.5":[114],"percentage":[115],"points":[116],"while":[117,206],"reducing":[118],"by":[121,157],"48-87%.":[122],"The":[123,191],"same":[124],"projection":[125,227],"consistently":[126],"hurts":[127],"fine-grained":[129,168],"CUB-200,":[130],"where":[131],"win":[134],"all":[136],"backbones.":[138],"establishes":[140],"practical":[142],"boundary":[143],"condition:":[144],"useful":[147],"class-level":[149],"structure":[150],"coarse":[152],"enough":[153],"be":[155,232],"captured":[156],"mean-separating":[158],"directions,":[159],"but":[160],"it":[161],"can":[162],"discard":[163],"subtle":[164],"cues":[165],"needed":[166],"recognition.":[169],"also":[171],"compare":[172],"PCA,":[175],"PCA+LDA,":[176],"regularized":[177],"LDA,":[178],"Local":[179],"Fisher":[180],"Analysis,":[182,185],"Neighbourhood":[183],"Components":[184],"three":[187],"extensions.":[190],"results":[192],"show":[193],"that":[194],"plain":[195],"offers":[197],"best":[199],"accuracy-cost":[200],"tradeoff":[201],"most":[203],"settings,":[205],"more":[207],"complex":[208],"methods":[211],"rarely":[212],"justify":[213],"their":[214],"additional":[215],"cost.":[216],"Overall,":[217],"study":[219],"provides":[220],"concrete":[221],"guidance":[222],"post-hoc":[225],"should,":[228],"should":[230],"not,":[231],"inserted":[233],"into":[234],"frozen-feature":[235],"classification":[237],"pipelines.":[238]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-08T00:00:00"}
