{"id":"W1800978511","doi":"10.1109/iscas.2003.1205843","title":"Local discriminant basis algorithm-a review of theory and applications in signal processing","year":2003,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Basis (linear algebra); Computer science; Principal component analysis; Signal processing; Discriminant; Multidimensional signal processing; Waveform; Linear discriminant analysis; Pattern recognition (psychology); SIGNAL (programming language); Algorithm; Artificial intelligence; Time–frequency analysis; Speech recognition; Mathematics; Digital signal processing; Computer vision; Radar","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":[0.0009213735,0.0007634734,0.001424907,0.00228367,0.0004531326,0.001673312,0.00102689,0.001333099,0.002361801],"category_scores_gemma":[0.001780364,0.0003955456,0.000633439,0.005000846,0.0009517155,0.001992699,0.0008110451,0.001696092,0.002902321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005165964,"about_ca_system_score_gemma":0.0007506465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001005522,"about_ca_topic_score_gemma":0.0006333451,"domain_scores_codex":[0.9993487,0.0001271649,0.00006729109,0.0001342737,0.0002913396,0.00003116781],"domain_scores_gemma":[0.9992191,0.0003772052,0.00005102635,0.00006430642,0.0002618784,0.00002647979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006241407,0.00006587289,0.0006178211,0.001043446,0.0000711456,0.0001536146,0.00009242907,0.01642522,0.0061012,0.05153115,0.01303947,0.9107963],"study_design_scores_gemma":[0.00005242259,0.0003337166,0.003063398,0.0007961928,0.0001507704,0.002393233,0.0002462485,0.2785166,0.01216105,0.2205562,0.4815098,0.000220423],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.002775795,0.1650112,0.8203026,0.001117583,0.0007360879,0.00007029251,0.000153525,0.0004357277,0.009397126],"genre_scores_gemma":[0.06944759,0.2803658,0.6330996,0.0009859179,0.003390156,0.0003005274,0.0008835419,0.0002575484,0.01126925],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002361801,"threshold_uncertainty_score":0.007901013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0146647083926078,"score_gpt":0.2876848759503504,"score_spread":0.2730201675577426,"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."}}