{"id":"W4393619291","doi":"10.23952/jnva.8.2024.3.06","title":"A fast and effective algorithm for sparse linear regression with $\\ell_p$-norm data fidelity and elastic net regularization","year":2024,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Henan Province; National Natural Science Foundation of China","keywords":"Elastic net regularization; Fidelity; Regularization (linguistics); Norm (philosophy); Mathematics; Linear regression; Regression; Algorithm; Lasso (programming language); Applied mathematics; Computer science; Mathematical optimization; Statistics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006063335,0.0001057061,0.0002288486,0.0002854917,0.0001243243,0.0002178117,0.0001318609,0.00005647817,0.000005976406],"category_scores_gemma":[0.00008514308,0.00006759501,0.00005068019,0.0004750538,0.00003039225,0.0008191222,0.0001123518,0.0001195443,4.467241e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001302015,"about_ca_system_score_gemma":0.00006698848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001074206,"about_ca_topic_score_gemma":0.000007566087,"domain_scores_codex":[0.999038,0.00006024686,0.0002797014,0.0002814165,0.000251674,0.0000889352],"domain_scores_gemma":[0.9989636,0.0003633951,0.0001734953,0.0001454695,0.0002621387,0.00009190748],"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":[0.0003875101,0.0003392679,0.004118495,0.0003065641,0.003924123,0.00007628448,0.001277259,0.009975591,0.001356068,0.002537707,0.001361427,0.9743397],"study_design_scores_gemma":[0.0004221639,0.0001839851,0.006561901,0.0001238254,0.0006411183,0.0000663612,0.00002528036,0.9897291,0.00006429019,0.001551596,0.000542002,0.00008836358],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009715023,0.0005353898,0.9886178,0.0008308099,0.0000706799,0.0001005396,0.0001122099,0.00001115929,0.000006344309],"genre_scores_gemma":[0.1197279,0.000585734,0.878432,0.0001565664,0.0005647896,0.000007036026,0.0003756788,0.00001241382,0.0001379031],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9797535,"threshold_uncertainty_score":0.2756445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01615078588087612,"score_gpt":0.2794741153414174,"score_spread":0.2633233294605413,"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."}}