{"id":"W2009986779","doi":"10.1109/bcc.2007.4430540","title":"Uncorrelated Multilinear Discriminant Analysis with Regularization for Gait Recognition","year":2007,"lang":"en","type":"article","venue":"","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multilinear map; Pattern recognition (psychology); Linear discriminant analysis; Artificial intelligence; Regularization (linguistics); Subspace topology; Discriminative model; Tensor (intrinsic definition); Discriminant; Mathematics; Linear subspace; Computer science","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":[0.00116767,0.0008765445,0.0008870436,0.0009631939,0.0004106283,0.0006095875,0.0006799945,0.0005797692,0.001062991],"category_scores_gemma":[0.003421037,0.0003342002,0.0008492699,0.001220273,0.0006746494,0.000872797,0.0007412056,0.001148311,0.0008044683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004461221,"about_ca_system_score_gemma":0.00076221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001454053,"about_ca_topic_score_gemma":0.00144871,"domain_scores_codex":[0.9991148,0.0003566612,0.00005026164,0.0001647271,0.0002740457,0.00003941877],"domain_scores_gemma":[0.9991131,0.0003415065,0.0001136653,0.0001429503,0.0002439987,0.00004466741],"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":[0.000159183,0.0001355852,0.001404945,0.0002008134,0.0001544829,0.0001095979,0.0001096559,0.2893389,0.0332853,0.03658894,0.004893893,0.6336187],"study_design_scores_gemma":[0.000005969498,0.00003240493,0.0003237439,0.000007146067,0.000010521,0.00003762204,0.00000894543,0.9862754,0.00310783,0.008443102,0.001730134,0.00001715495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004810988,0.0002402249,0.9943672,0.00008379736,0.00003005202,0.0000140662,0.00002613518,0.0001434122,0.0002842167],"genre_scores_gemma":[0.149061,0.0005203335,0.8477484,0.00008885375,0.0001147075,0.0001118539,0.0003051365,0.0001192795,0.001930482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001454053,"threshold_uncertainty_score":0.00617528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01296328701913961,"score_gpt":0.2206540983579982,"score_spread":0.2076908113388586,"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."}}