{"id":"W2122090912","doi":"","title":"Maximum-Margin Matrix Factorization","year":2004,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":960,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Margin (machine learning); Generalization; Matrix decomposition; Matrix norm; Computer science; Factorization; Norm (philosophy); Generalization error; Rank (graph theory); Matrix (chemical analysis); Mathematics; Algorithm; Artificial intelligence; Algebra over a field; Machine learning; Combinatorics; Pure mathematics; Artificial neural network","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":[0.002517906,0.001600309,0.001890337,0.0008477592,0.0008088389,0.001807964,0.0025791,0.001998149,0.006813414],"category_scores_gemma":[0.00890879,0.0006659819,0.001021689,0.001300701,0.001509058,0.003484583,0.002584714,0.0028513,0.003935203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000676849,"about_ca_system_score_gemma":0.001190889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001069047,"about_ca_topic_score_gemma":0.001450482,"domain_scores_codex":[0.9975275,0.0008732522,0.0001019025,0.0006109174,0.0007147581,0.0001715527],"domain_scores_gemma":[0.9969441,0.001410768,0.0002907658,0.0006911799,0.0005259904,0.0001371388],"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":[0.0003203294,0.0002740649,0.0008040883,0.0004425821,0.0001630528,0.0002430933,0.0002514585,0.3587869,0.01034004,0.1682404,0.02886597,0.4312682],"study_design_scores_gemma":[0.00001793103,0.00005302721,0.00008087998,0.00001663036,0.00001106187,0.00005397246,0.00001763384,0.9135706,0.002295929,0.07895756,0.004911119,0.00001369797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008992971,0.0001023037,0.9977056,0.0001077099,0.00003973415,0.00001804203,0.00004796617,0.0002430498,0.0008363203],"genre_scores_gemma":[0.1628631,0.0004357826,0.8277779,0.0003534161,0.000421378,0.0002870412,0.0007910975,0.0002493004,0.006821033],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006813414,"threshold_uncertainty_score":0.02279311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0113121206388473,"score_gpt":0.2476123398623167,"score_spread":0.2363002192234694,"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."}}