{"id":"W2177344785","doi":"10.1109/pacrim.2015.7334822","title":"Fast classification of handwritten digits using 2D-DCT based sparse PCA","year":2015,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Discrete cosine transform; Decorrelation; Principal component analysis; Pattern recognition (psychology); Dimensionality reduction; Computer science; Artificial intelligence; MNIST database; Dimension (graph theory); Speech recognition; Mathematics; Algorithm; Artificial neural network; Image (mathematics)","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":[0.0004216362,0.0007830006,0.0007258104,0.001486525,0.0002808082,0.0008072786,0.0006056568,0.0006534499,0.001969179],"category_scores_gemma":[0.001534073,0.0002706618,0.0005956889,0.00167328,0.0003233095,0.0009522657,0.0004002204,0.0007245924,0.002272013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001969687,"about_ca_system_score_gemma":0.0005682657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001624044,"about_ca_topic_score_gemma":0.002422672,"domain_scores_codex":[0.9994928,0.00006013131,0.00003039137,0.000106845,0.0002554612,0.00005445664],"domain_scores_gemma":[0.9992566,0.000202052,0.00008891893,0.000113222,0.0003075148,0.00003166725],"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":[0.0001909497,0.0001255886,0.001222626,0.0001383061,0.00003287836,0.0001442635,0.00005498187,0.01803691,0.1310523,0.002368775,0.003644104,0.8429883],"study_design_scores_gemma":[0.00003751892,0.0002180344,0.004484938,0.00002442185,0.0000352465,0.0006992294,0.00005732464,0.8710185,0.113496,0.002275397,0.007606647,0.00004671255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02735418,0.0002954895,0.968878,0.0001676477,0.0001539367,0.00007746618,0.0001839628,0.001382651,0.001506621],"genre_scores_gemma":[0.161325,0.0006997577,0.8313211,0.0001817081,0.0001837917,0.0001046265,0.001041891,0.0001203891,0.00502184],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001969179,"threshold_uncertainty_score":0.006587565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1183122671962887,"score_gpt":0.3137177932727139,"score_spread":0.1954055260764253,"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."}}