{"id":"W2401002434","doi":"","title":"Learning Dictionary Via Wavelet Sparse Principal Component Analysis.","year":2015,"lang":"en","type":"article","venue":"International Conference on Pattern Recognition Applications and Methods","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; Toronto Metropolitan University","funders":"","keywords":"Principal component analysis; Wavelet; Computer science; Artificial intelligence; Pattern recognition (psychology); Dictionary learning; Component (thermodynamics); Sparse PCA; Sparse approximation; Speech recognition; Physics","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":[0.0006152798,0.0008821149,0.001153484,0.0009525334,0.000387901,0.0009282531,0.001101114,0.00100783,0.002920699],"category_scores_gemma":[0.004781803,0.0005765977,0.0007782407,0.001745087,0.0005827845,0.001582631,0.001374376,0.001976848,0.002765817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002167337,"about_ca_system_score_gemma":0.000951536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002417236,"about_ca_topic_score_gemma":0.003160763,"domain_scores_codex":[0.9995796,0.00009955513,0.00002889529,0.00009305702,0.000145912,0.00005302771],"domain_scores_gemma":[0.9990686,0.0002691331,0.00007283122,0.0002246269,0.0003070113,0.00005781436],"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":[0.0003299782,0.0001623786,0.001185118,0.0002640458,0.0001494184,0.0001261987,0.0001325695,0.08578687,0.02355015,0.02126451,0.02871065,0.8383381],"study_design_scores_gemma":[0.00003267987,0.00006971777,0.0004631346,0.00002331666,0.00003539978,0.0001032536,0.00005884463,0.9751515,0.004407085,0.01432585,0.005311034,0.00001822812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006116798,0.0002988925,0.9918664,0.0001183502,0.000114027,0.0000280995,0.0001722035,0.0005167177,0.0007685482],"genre_scores_gemma":[0.1753909,0.001285587,0.8140361,0.0002239832,0.0002045021,0.0001957357,0.002893375,0.0003730748,0.005396727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002920699,"threshold_uncertainty_score":0.009770691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1172425045384214,"score_gpt":0.3808358604053482,"score_spread":0.2635933558669268,"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."}}