{"id":"W2803106003","doi":"10.1137/16m1107863","title":"Orthogonal Nonnegative Matrix Factorization by Sparsity and Nuclear Norm Optimization","year":2018,"lang":"en","type":"article","venue":"SIAM Journal on Matrix Analysis and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Hong Kong Baptist University","keywords":"Mathematics; Factorization; Matrix decomposition; Matrix norm; Sparse matrix; Coefficient matrix; Non-negative matrix factorization; Matrix (chemical analysis); Incomplete LU factorization; Norm (philosophy); Algorithm; Applied mathematics; Mathematical optimization","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":[0.000200285,0.0001228366,0.0001759494,0.0002794837,0.0007312613,0.000374297,0.0001917974,0.00006972597,0.0001061841],"category_scores_gemma":[0.00001340318,0.0001036988,0.00007780985,0.0009258651,0.00006697565,0.000454142,0.00006991322,0.0001448288,0.00003809658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002537076,"about_ca_system_score_gemma":0.0000188195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000755107,"about_ca_topic_score_gemma":0.000004797838,"domain_scores_codex":[0.9990088,0.00005816175,0.0002543378,0.0002956934,0.0002366176,0.0001463854],"domain_scores_gemma":[0.9991288,0.00005238201,0.0002393004,0.0001888703,0.0002257672,0.0001648467],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003972504,0.002861125,0.04833492,0.0001606867,0.004882317,0.00002306832,0.007742681,0.05351277,0.04269172,0.4635635,0.03762268,0.3382073],"study_design_scores_gemma":[0.001713257,0.0006979564,0.01541097,0.00007678289,0.001167936,0.00009888098,0.0006449197,0.8909832,0.002111832,0.01285391,0.07312102,0.0011193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02636823,0.00009049835,0.9720471,0.0007673862,0.00003222882,0.0001243955,0.0000252498,0.0000404423,0.0005044906],"genre_scores_gemma":[0.955633,0.0007272834,0.04289777,0.0002221676,0.0002099615,0.00001201678,0.00005478362,0.00000972112,0.000233315],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9292647,"threshold_uncertainty_score":0.5624345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00649217373800545,"score_gpt":0.2568799767331522,"score_spread":0.2503878029951468,"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."}}