{"id":"W2151269805","doi":"","title":"Convex Relaxation of Mixture Regression with Efficient Algorithms","year":2009,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Semidefinite programming; Relaxation (psychology); Algorithm; Mathematical optimization; Convex optimization; Regular polygon; Matrix (chemical analysis); Mathematics; Maximum a posteriori estimation; Computer science; Matrix completion; Maximum likelihood; Gaussian; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002877543,0.00007103217,0.00009237323,0.0000423916,0.00001410199,0.000005404482,0.00004457418,0.00004710308,0.00001419287],"category_scores_gemma":[0.000003581259,0.00004675844,0.00001718003,0.00009091393,0.00001231322,0.00002226231,0.000004005213,0.00006079088,0.0000015307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001046131,"about_ca_system_score_gemma":0.000003416321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000037052,"about_ca_topic_score_gemma":5.289993e-7,"domain_scores_codex":[0.9996631,0.000006180862,0.0000857316,0.00006964921,0.0001000353,0.00007528465],"domain_scores_gemma":[0.9997661,0.000009365401,0.00002529641,0.0001396032,0.00003964524,0.00002005095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001181163,0.000218235,0.0004737295,0.00005159845,0.00008101981,0.00005071052,0.001049428,0.1963856,0.5461006,0.006386232,0.03841821,0.2106666],"study_design_scores_gemma":[0.0001730501,0.0001281719,0.001837818,0.0001642955,0.000009016366,0.00001004021,0.00002366857,0.3170695,0.6795918,0.0003197169,0.0005571264,0.0001157526],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.555119,0.0003949094,0.4142448,0.0001509716,0.0001140571,0.0002001973,0.000001288972,0.00125895,0.02851582],"genre_scores_gemma":[0.983106,0.00001577069,0.01673436,0.00003444107,0.00001913667,8.179344e-7,0.000002633178,0.000007145597,0.0000796761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.427987,"threshold_uncertainty_score":0.1906754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007312513597466893,"score_gpt":0.2148197892929533,"score_spread":0.2075072756954864,"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."}}