{"id":"W2161895106","doi":"10.1109/icme.2006.262861","title":"Selecting Kernel Eigenfaces for Face Recognition with One Training Sample Per Subject","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Linear discriminant analysis; Facial recognition system; Kernel Fisher discriminant analysis; Kernel principal component analysis; Computer science; Kernel (algebra); Eigenface; Principal component analysis; Subspace topology; Feature (linguistics); Curse of dimensionality; Feature extraction; Sample (material); Feature vector; Face (sociological concept); Feature selection; Kernel method; Mathematics; Support vector machine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007303781,0.000383125,0.0006867468,0.000628654,0.0002081348,0.0002979741,0.0003437732,0.0003561112,0.001070295],"category_scores_gemma":[0.001624462,0.0002070633,0.0004133284,0.000540795,0.0002591904,0.0004769973,0.0003449865,0.0003289958,0.001005177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001898761,"about_ca_system_score_gemma":0.0003585547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000703261,"about_ca_topic_score_gemma":0.001192374,"domain_scores_codex":[0.9995905,0.0001567735,0.00001863519,0.00008482668,0.0001075915,0.00004177273],"domain_scores_gemma":[0.9996681,0.0001254228,0.00002861274,0.00006925515,0.0000927318,0.00001586626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003722547,0.0001739064,0.002243465,0.00007917681,0.00006594306,0.0001252301,0.0001293177,0.03095961,0.105487,0.005098246,0.004217111,0.8510488],"study_design_scores_gemma":[0.00003407054,0.0001481297,0.006722583,0.00001289524,0.00004061161,0.0004750603,0.00007486899,0.9290722,0.05245632,0.006109161,0.004814711,0.0000392624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08265705,0.0003871055,0.9148644,0.00009901308,0.00003673343,0.00004478277,0.0000643486,0.000868133,0.0009784971],"genre_scores_gemma":[0.4406788,0.0004247789,0.5548826,0.00005785811,0.00004447878,0.0001382878,0.0003314333,0.00009891953,0.003342885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001070295,"threshold_uncertainty_score":0.003862679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05173743025900296,"score_gpt":0.2512673951621029,"score_spread":0.1995299649030999,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}