{"id":"W3117873759","doi":"10.1002/cjs.11579","title":"Automatic sparse principal component analysis","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Principal component analysis; Dimensionality reduction; Sparse PCA; Singular value decomposition; Computer science; Sparse approximation; Pattern recognition (psychology); Artificial intelligence; Robust principal component analysis; Projection (relational algebra); Sparse matrix; Dimension (graph theory); Feature selection; Regularization (linguistics); Curse of dimensionality; Algorithm; Mathematics","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":[0.00005393658,0.00009044573,0.0002394052,0.0002033983,0.00003622119,0.00004410016,0.0001481501,0.00003213141,0.0001981798],"category_scores_gemma":[0.00005947324,0.00009166731,0.00006401623,0.0002692271,0.00003419093,0.00004044559,0.000004998945,0.0001645119,0.00001191769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007396527,"about_ca_system_score_gemma":0.000156678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003073832,"about_ca_topic_score_gemma":0.002189518,"domain_scores_codex":[0.9993551,0.0000172394,0.0002900198,0.00005076479,0.0001161973,0.000170692],"domain_scores_gemma":[0.9991291,0.00003561677,0.00007651244,0.00008783208,0.000106795,0.0005641282],"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":[0.00001918556,0.00003291471,0.02322299,0.0002265643,0.005422921,0.008928396,0.006692992,0.5092666,0.002762616,0.01113994,0.360424,0.07186089],"study_design_scores_gemma":[0.0001997617,0.0001005433,0.01726486,0.00004685286,0.0006739855,0.00006217682,0.00009306916,0.9573981,0.0009783728,0.0007241303,0.02221217,0.0002459472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2959542,0.0004540487,0.7016064,0.0003247832,0.0003359574,0.0000765568,0.0002326432,0.0001049969,0.000910444],"genre_scores_gemma":[0.9545489,0.00001771385,0.04514642,0.0001772289,0.00008395627,2.619031e-7,0.000006910649,0.00001474926,0.000003891766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6585947,"threshold_uncertainty_score":0.3738084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02761533720272301,"score_gpt":0.2139115967867921,"score_spread":0.1862962595840691,"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."}}