{"id":"W4390493794","doi":"10.1109/snams60348.2023.10375396","title":"Adaptive Constrained ICAMGGMM: An Improvement Over ICA","year":2023,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Independent component analysis; Robustness (evolution); Computer science; Blind signal separation; Covariance matrix; Multivariate statistics; Covariance; Source separation; Pattern recognition (psychology); Gaussian; Algorithm; Artificial intelligence; Mathematics; Machine learning; Statistics","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":[0.001088953,0.001559303,0.001041909,0.001021415,0.0005009978,0.0009946958,0.001805163,0.001387891,0.00314939],"category_scores_gemma":[0.003142643,0.0003529211,0.001308373,0.001891686,0.000656604,0.00146933,0.002011552,0.00194378,0.002007684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005954002,"about_ca_system_score_gemma":0.001641909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007802953,"about_ca_topic_score_gemma":0.009027643,"domain_scores_codex":[0.999079,0.0002089257,0.00004178756,0.0002270843,0.0003695067,0.00007366604],"domain_scores_gemma":[0.9991406,0.0002964969,0.00006274813,0.0002080107,0.0002488403,0.0000432964],"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":[0.0002367432,0.0001014128,0.00093358,0.000158027,0.0001186058,0.00009478658,0.0001022922,0.1000756,0.01807324,0.01337808,0.008192541,0.8585351],"study_design_scores_gemma":[0.00003490475,0.0001223017,0.001003741,0.00003607138,0.00004788508,0.0002570998,0.00002574731,0.9551957,0.01013206,0.0103318,0.02276411,0.00004858552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003477666,0.0005914707,0.992257,0.0001437418,0.0001037185,0.00004452773,0.000102519,0.001413966,0.001865365],"genre_scores_gemma":[0.08772137,0.0008915765,0.9047592,0.0003913312,0.0002195666,0.0001534369,0.0006285482,0.0004430587,0.004791864],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007802953,"threshold_uncertainty_score":0.01551509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02926304484837247,"score_gpt":0.2956137511194639,"score_spread":0.2663507062710915,"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."}}