{"id":"W2122508088","doi":"10.1109/icdm.2010.162","title":"Compressed Nonnegative Sparse Coding","year":2010,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Nuclear Safety Commission; National Science Council; National Science Foundation","keywords":"Neural coding; Random projection; Computer science; Coding (social sciences); Artificial intelligence; Pattern recognition (psychology); Sparse approximation; Compressed sensing; K-SVD; Sparse matrix; Projection (relational algebra); Algorithm; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0000340787,0.00009620033,0.00009800344,0.00004542962,0.00003792784,0.00003037736,0.000111628,0.00005733912,0.000236954],"category_scores_gemma":[0.0000111622,0.00008863209,0.00002975626,0.00006821251,0.00003258609,0.00007299756,0.00002426325,0.0002107387,0.00007207978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005796327,"about_ca_system_score_gemma":0.000003393392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001917052,"about_ca_topic_score_gemma":0.00003850194,"domain_scores_codex":[0.9995985,0.000005180786,0.00008943553,0.00009211505,0.00006828189,0.0001465006],"domain_scores_gemma":[0.9996655,0.00004120696,0.00001034449,0.0002050816,0.00003131996,0.00004657251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002932574,0.00001435007,0.0001947905,0.000005293934,0.00002513248,0.00001240999,0.0001553997,0.000985962,0.9610353,0.01210967,0.02127728,0.004181438],"study_design_scores_gemma":[0.0001251845,0.000009558608,0.0007855623,0.00001502867,0.000005893676,0.000009563018,0.00002734885,0.1292672,0.8521104,0.001722969,0.0157337,0.0001875676],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6245156,0.00002446286,0.07627508,0.00008025016,0.0008822434,0.0001602627,0.000003143449,0.003593761,0.2944652],"genre_scores_gemma":[0.9861374,0.000007551765,0.01348321,0.00007032583,0.0000956263,0.00000499151,0.00000196069,0.00002033641,0.0001785792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3616219,"threshold_uncertainty_score":0.3614312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01220363536934482,"score_gpt":0.2186181119985822,"score_spread":0.2064144766292374,"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."}}