{"id":"W4248534906","doi":"10.4018/9781605669021.ch019","title":"Robust Independent Component Analysis for Cognitive Informatics","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"FastICA; Infomax; Independent component analysis; Outlier; Sensitivity (control systems); Pattern recognition (psychology); Negentropy; Mathematics; Artificial intelligence; Divergence (linguistics); Computer science; Contrast (vision); Algorithm; Blind signal separation; Engineering","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":[0.001001284,0.001570697,0.0009460362,0.001530939,0.0003151492,0.002242675,0.001252574,0.001503794,0.03109904],"category_scores_gemma":[0.00258607,0.0003924568,0.000736411,0.003129601,0.000794473,0.001875861,0.001171243,0.001764479,0.01888488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006602338,"about_ca_system_score_gemma":0.00089822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0010409,"about_ca_topic_score_gemma":0.001603143,"domain_scores_codex":[0.9993492,0.0001793538,0.00003960401,0.0001099538,0.0002988827,0.00002305961],"domain_scores_gemma":[0.9990707,0.0005457253,0.00004336986,0.0001502772,0.0001665106,0.0000234959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003175481,0.00004253156,0.000226112,0.0009784925,0.0000810862,0.00008146033,0.0001232833,0.004472974,0.002387977,0.05086839,0.070274,0.8704319],"study_design_scores_gemma":[0.00003229384,0.0001152354,0.002123995,0.00111379,0.00009549525,0.0005837502,0.00021952,0.07259404,0.005008523,0.2833366,0.6346692,0.0001076072],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002160844,0.07584341,0.8303513,0.002220615,0.0009722423,0.0001743337,0.0007745372,0.004169176,0.08333356],"genre_scores_gemma":[0.03889945,0.06621711,0.7944376,0.00100304,0.0012802,0.0007878406,0.002077698,0.001245146,0.0940519],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03109904,"threshold_uncertainty_score":0.1040367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05146461021903113,"score_gpt":0.2651893708031613,"score_spread":0.2137247605841302,"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."}}