{"id":"W2951721760","doi":"10.48550/arxiv.0907.2079","title":"An Augmented Lagrangian Approach for Sparse Principal Component Analysis","year":2009,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Augmented Lagrangian method; Sparse PCA; Principal component analysis; Orthogonality; Convergence (economics); Dimension (graph theory); Computer science; Sparse approximation; Dimensionality reduction; Mathematical optimization; Feature (linguistics); Sparse matrix; Algorithm; Mathematics; Artificial intelligence","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":[0.001009437,0.00114244,0.001082142,0.0008621933,0.0005365336,0.00093292,0.001216914,0.001223266,0.003396953],"category_scores_gemma":[0.002219619,0.0004909023,0.001034752,0.001012425,0.0008252111,0.00128496,0.001485737,0.001847264,0.0008632911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005159321,"about_ca_system_score_gemma":0.001639619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002966993,"about_ca_topic_score_gemma":0.003414411,"domain_scores_codex":[0.999437,0.0002029677,0.00002235594,0.0000813497,0.0002102232,0.0000460182],"domain_scores_gemma":[0.9993723,0.0003101603,0.00006636341,0.00005274715,0.0001634277,0.00003503312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000453806,0.00006517697,0.0004571291,0.0002522206,0.000068288,0.0001760505,0.000119042,0.8181942,0.00608467,0.07405724,0.006623158,0.09385738],"study_design_scores_gemma":[0.000004308245,0.00001061386,0.00002946038,0.000005534233,0.000003728809,0.00001591514,0.000005041279,0.9926841,0.0002399244,0.005475731,0.001521131,0.000004598041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008744754,0.00008075841,0.9981126,0.0000871182,0.00002011033,0.00001563344,0.00002590147,0.00006334195,0.0007200554],"genre_scores_gemma":[0.08519529,0.0005846645,0.9086766,0.000216357,0.000155778,0.000280838,0.0003971728,0.0001685886,0.004324649],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003396953,"threshold_uncertainty_score":0.01136398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0514146644095187,"score_gpt":0.2781653502223928,"score_spread":0.2267506858128741,"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."}}