{"id":"W2045265907","doi":"10.1109/icassp.2010.5495889","title":"Discriminative base decomposition for time-frequency matrix decomposition","year":2010,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Non-negative matrix factorization; Matrix decomposition; Pattern recognition (psychology); Discriminative model; Decomposition; Discriminant; Computer science; Artificial intelligence; Time–frequency analysis; Matrix (chemical analysis); Linear discriminant analysis; Mathematics; Computer vision; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004045409,0.0001330636,0.0001220313,0.0001545232,0.0001665454,0.000219047,0.0004889166,0.00009477007,0.00009166759],"category_scores_gemma":[0.00003570735,0.0001215542,0.0000871557,0.0001802284,0.00003607376,0.001030556,0.00008111354,0.0001530395,0.00009809476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003021777,"about_ca_system_score_gemma":0.00005194196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002018498,"about_ca_topic_score_gemma":0.00002510738,"domain_scores_codex":[0.9990243,0.00006162796,0.000223507,0.0003333683,0.0001649475,0.0001922995],"domain_scores_gemma":[0.9990407,0.0001651558,0.00009466285,0.0004173268,0.0001966629,0.00008548061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007051372,0.0001098826,0.00003059696,0.000008119109,0.000006853672,0.000001404659,0.000423798,0.000006304556,0.290951,0.6985468,0.00333662,0.006571508],"study_design_scores_gemma":[0.000627514,0.0003889661,0.001123868,0.00001761085,0.00001709941,0.00004456569,0.00002374351,0.1401869,0.6092239,0.2462136,0.001664622,0.0004676122],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02773709,0.000005688794,0.9585683,0.003100408,0.0001730384,0.0004938798,0.000006516475,0.0006877194,0.009227383],"genre_scores_gemma":[0.39555,0.000001006184,0.6033115,0.0004782549,0.00004647735,0.00009518354,0.00003104288,0.000009147583,0.0004774156],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4523332,"threshold_uncertainty_score":0.4956836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01104542815393846,"score_gpt":0.3292261586876735,"score_spread":0.3181807305337351,"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."}}