{"id":"W1543035419","doi":"10.1109/iscas.2003.1205840","title":"Optimally weighted local discriminant bases [signal feature extraction/classification]","year":2003,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pattern recognition (psychology); Discriminant; Linear discriminant analysis; Artificial intelligence; Feature extraction; Weighting; Principal component analysis; Disjoint sets; Computer science; Feature (linguistics); Projection (relational algebra); SIGNAL (programming language); Optimal discriminant analysis; Mathematics; Algorithm","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":[0.001037585,0.0008226019,0.001357036,0.0012295,0.0003412874,0.0008638572,0.0009525956,0.0007998068,0.001876851],"category_scores_gemma":[0.002094562,0.0003685893,0.000556241,0.001621735,0.0006725835,0.0009196238,0.000735076,0.001012827,0.001741307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000378671,"about_ca_system_score_gemma":0.0005574684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008679281,"about_ca_topic_score_gemma":0.001053384,"domain_scores_codex":[0.9991591,0.0001858844,0.00003726842,0.0001446115,0.0004197279,0.0000533789],"domain_scores_gemma":[0.9993735,0.0001392466,0.00008076879,0.0001335995,0.0002469103,0.00002598358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001649998,0.0000714874,0.0005382784,0.0002647233,0.00009639885,0.0001138748,0.00006655813,0.09445942,0.06773969,0.03849119,0.006681822,0.7913116],"study_design_scores_gemma":[0.00003113221,0.00008495952,0.0008432743,0.00003931635,0.00005631888,0.0002846359,0.00002012939,0.9172599,0.03951875,0.02551153,0.01629269,0.00005739162],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002068711,0.0002857698,0.9967099,0.0000355048,0.0000218741,0.00002001292,0.0000321909,0.0002419993,0.0005839476],"genre_scores_gemma":[0.08286829,0.0009441766,0.9118801,0.00007785439,0.00009759514,0.0001017728,0.000272017,0.0001665918,0.003591525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001876851,"threshold_uncertainty_score":0.006278753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03352520142595439,"score_gpt":0.2980736006263627,"score_spread":0.2645483992004083,"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."}}