{"id":"W2075734730","doi":"10.1002/col.21942","title":"Spectral compression using subspace clustering","year":2015,"lang":"en","type":"article","venue":"Color Research & Application","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Linear subspace; Multispectral image; Redundancy (engineering); Subspace topology; Principal component analysis; Data compression; Hyperspectral imaging; Dimension (graph theory); Computer science; Compression (physics); Cluster analysis; JPEG 2000; Algorithm; Pattern recognition (psychology); Mathematics; Data redundancy; Representation (politics); Artificial intelligence; Image compression; Image processing; Combinatorics; Database; Geometry; Physics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007448454,0.00008179461,0.00009400582,0.00009699935,0.0003505311,0.0001046006,0.0003492431,0.00002589937,0.00004870716],"category_scores_gemma":[0.00001184864,0.00007840279,0.00003290796,0.0006876722,0.0001297264,0.0001835544,0.000191837,0.0002145604,0.0002587455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00012654,"about_ca_system_score_gemma":0.000158452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005954743,"about_ca_topic_score_gemma":0.00003057091,"domain_scores_codex":[0.9986929,0.00006771228,0.0001420612,0.0003162811,0.0004176653,0.0003633899],"domain_scores_gemma":[0.9990361,0.00006433416,0.00005286757,0.0003823981,0.0002509872,0.0002132696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001637599,0.0008780425,0.04077469,0.00003067735,0.00003727692,0.000002026366,0.002296715,0.05010247,0.6024368,0.1433702,0.01398387,0.1459235],"study_design_scores_gemma":[0.0005702359,0.00009202016,0.004966506,0.0000215446,0.000009012354,0.000002013856,0.001820451,0.8752303,0.02410647,0.01862322,0.07430443,0.0002537973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8918619,0.00002488064,0.09607381,0.0006719883,0.00003561563,0.0007334453,0.000006340314,0.00004649768,0.01054552],"genre_scores_gemma":[0.9967076,0.000001455268,0.002280464,0.0000123133,0.000291099,0.0003193033,0.0000233095,0.00001099509,0.0003535164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8251278,"threshold_uncertainty_score":0.3325736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2061031037914527,"score_gpt":0.4588836327652715,"score_spread":0.2527805289738188,"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."}}