{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001299872,0.001768646,0.001298953,0.002413882,0.0008589473,0.001314178,0.001075715,0.001102052,0.004151618],"category_scores_gemma":[0.006394825,0.0006121481,0.0014482,0.002646495,0.0008603635,0.001627639,0.001605632,0.00189342,0.002782085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000442987,"about_ca_system_score_gemma":0.002154953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003366249,"about_ca_topic_score_gemma":0.003537311,"domain_scores_codex":[0.9980801,0.0004778053,0.00009422805,0.0004078639,0.000765438,0.0001746144],"domain_scores_gemma":[0.9975734,0.0007576218,0.0002000682,0.0004560292,0.0009450627,0.00006785804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002698225,0.0001614689,0.001741432,0.0003654122,0.0001331519,0.0001873505,0.0002100224,0.1336408,0.04289478,0.02775767,0.0202578,0.7723802],"study_design_scores_gemma":[0.00002285432,0.0000596333,0.00122523,0.00002427157,0.00002512609,0.0001330855,0.00004617166,0.9610677,0.01215398,0.01608612,0.009110071,0.00004571101],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004155763,0.00015306,0.9931497,0.0001149277,0.00005063058,0.00005794051,0.0002056941,0.0009837963,0.001128509],"genre_scores_gemma":[0.1324946,0.0006207329,0.8596374,0.0001773144,0.0001723074,0.000368913,0.002284927,0.0004267579,0.003816985],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004151618,"threshold_uncertainty_score":0.01388854,"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."}}