{"id":"W2104417092","doi":"10.1007/s10107-011-0452-4","title":"An augmented Lagrangian approach for sparse principal component analysis","year":2011,"lang":"en","type":"article","venue":"Mathematical Programming","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":110,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Sparse PCA; Principal component analysis; Mathematics; Orthogonality; Dimension (graph theory); Algorithm; Uncorrelated; Dimensionality reduction; Applied mathematics; Computer science; Pattern recognition (psychology); Combinatorics; Artificial intelligence; Statistics; Geometry","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":[0.001248914,0.0009059954,0.001053802,0.0008160115,0.0004358258,0.001184431,0.001274034,0.001086675,0.002872517],"category_scores_gemma":[0.003565019,0.0006468543,0.000862854,0.001071346,0.001098195,0.001463132,0.001767342,0.002116847,0.0007124966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004956822,"about_ca_system_score_gemma":0.001321621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002144972,"about_ca_topic_score_gemma":0.002975113,"domain_scores_codex":[0.999494,0.0002368638,0.00001969675,0.00004932381,0.0001701643,0.00003009103],"domain_scores_gemma":[0.9989404,0.0006664029,0.00007781787,0.00007568357,0.0001954789,0.00004425315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003562189,0.00006182313,0.0001824085,0.0001963562,0.00004486619,0.00009105766,0.00008271286,0.6013916,0.00235468,0.3202765,0.005223833,0.07005864],"study_design_scores_gemma":[0.000004202398,0.00001111441,0.00001854146,0.000007170616,0.000004282636,0.00001287411,0.000005569451,0.9656085,0.0001722473,0.03278374,0.001366943,0.000004801556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006163192,0.00007070782,0.9983621,0.00009811961,0.00001982274,0.000007820737,0.00001917676,0.00002368924,0.0007823154],"genre_scores_gemma":[0.06705213,0.0006854304,0.9253128,0.0001649743,0.0001616446,0.0002289159,0.0001977541,0.0001333368,0.006062986],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002872517,"threshold_uncertainty_score":0.00960958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05386574135832146,"score_gpt":0.2661403389646625,"score_spread":0.2122745976063411,"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."}}