{"id":"W1923607724","doi":"10.1109/ijcnn.1999.831076","title":"An information theoretic method for designing multiresolution principal component transforms","year":2003,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Principal component analysis; Computer science; Transformation (genetics); Multiresolution analysis; Component (thermodynamics); Minification; Algorithm; Property (philosophy); Signal processing; SIGNAL (programming language); Mathematical optimization; Mathematics; Artificial intelligence; Wavelet transform; Wavelet; Digital signal processing","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":[0.001591546,0.001026034,0.0007287088,0.001059734,0.0005009333,0.0008686234,0.001011135,0.001037332,0.002208095],"category_scores_gemma":[0.002946442,0.0006744016,0.0009884646,0.0009119922,0.001098276,0.001481059,0.0009780541,0.001713572,0.0008784065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004907736,"about_ca_system_score_gemma":0.0006996542,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003097245,"about_ca_topic_score_gemma":0.0004500954,"domain_scores_codex":[0.9993182,0.0002018791,0.00003591179,0.000106727,0.0003093761,0.00002792804],"domain_scores_gemma":[0.9994799,0.000239696,0.00005034449,0.00009458968,0.0001149627,0.00002041471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008930256,0.00007902312,0.0002312738,0.0002871201,0.0001136989,0.0001728786,0.0001779992,0.3230166,0.04560189,0.3545287,0.003828191,0.2718733],"study_design_scores_gemma":[0.00002796562,0.00008704244,0.0001009596,0.00002785654,0.00003677576,0.0001829364,0.0000212916,0.8962137,0.01249744,0.07736387,0.01340683,0.00003337427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002832865,0.00004826195,0.9992731,0.00003039935,0.000007615081,0.000008076741,0.000005889059,0.00003503675,0.0003083627],"genre_scores_gemma":[0.02619016,0.0002658021,0.9723283,0.00007344664,0.0000507969,0.0001295878,0.00005106725,0.00007019036,0.0008406176],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002208095,"threshold_uncertainty_score":0.00841701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02556283207575806,"score_gpt":0.3146276898962558,"score_spread":0.2890648578204977,"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."}}