{"id":"W2508202655","doi":"10.1109/iscas.2016.7527309","title":"Multiview emotion recognition via multi-set locality preserving canonical correlation analysis","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Canonical correlation; Locality; Computer science; Correlation; Pattern recognition (psychology); Emotion recognition; Set (abstract data type); Artificial intelligence; Basis (linear algebra); Data correlation; Data set; Fusion; Data mining; Mathematics","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":[0.001079016,0.0008981798,0.001018134,0.001339134,0.0005268287,0.0009739083,0.0009276086,0.0005301973,0.001439287],"category_scores_gemma":[0.002750251,0.0003540828,0.001209848,0.0014775,0.0007237604,0.001497383,0.001466784,0.001023439,0.0006060673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004213652,"about_ca_system_score_gemma":0.0007778091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002140104,"about_ca_topic_score_gemma":0.002797526,"domain_scores_codex":[0.9989054,0.0003081934,0.0000452073,0.000296937,0.0003418612,0.0001023657],"domain_scores_gemma":[0.9990694,0.0002318828,0.0001131477,0.0001854928,0.0003323324,0.00006786945],"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":[0.0004262457,0.0001598108,0.003497069,0.0002381817,0.0003658338,0.000238351,0.0003923097,0.105163,0.07760765,0.02121874,0.006644724,0.7840481],"study_design_scores_gemma":[0.00001049113,0.00009857622,0.001685752,0.00001394086,0.0000534101,0.000165097,0.00007286149,0.9762842,0.01276806,0.006772532,0.002024028,0.00005094916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0136327,0.0003158764,0.9848086,0.000095284,0.0000381636,0.00002797591,0.00005295429,0.0003473935,0.0006810447],"genre_scores_gemma":[0.5005321,0.0008149311,0.4953717,0.0002504797,0.0001937956,0.000183073,0.0005360579,0.0002252185,0.001892596],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002140104,"threshold_uncertainty_score":0.005706489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05873250954835924,"score_gpt":0.2916485848936467,"score_spread":0.2329160753452875,"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."}}