{"id":"W4285304111","doi":"10.1109/lsp.2022.3179168","title":"Nonlinear Orthogonal NMF on the Stiefel Manifold With Graph-Based Total Variation Regularization","year":2022,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regularization (linguistics); Stiefel manifold; Mathematics; Algorithm; Nonlinear system; Orthogonality; Uniqueness; Computer science; Mathematical optimization; Artificial intelligence; Applied mathematics; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002800532,0.0002022352,0.0001200703,0.0001641828,0.0004663836,0.0001523235,0.0001655818,0.00004131726,0.00003545968],"category_scores_gemma":[0.00001122389,0.0001680896,0.00004757322,0.0005382787,0.00005539149,0.0001746988,0.00001045864,0.0003932672,0.000009324238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001814518,"about_ca_system_score_gemma":0.00006775428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003753785,"about_ca_topic_score_gemma":9.594102e-7,"domain_scores_codex":[0.9985573,0.00009761007,0.0002259251,0.0002817845,0.0005933223,0.0002440152],"domain_scores_gemma":[0.9994715,0.00008280476,0.000118099,0.0002231778,0.00006376918,0.00004068864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005502616,0.00003995728,0.00002867085,0.00004112435,0.00001974468,0.00001300836,0.0001827046,0.7028312,0.2933901,0.00005818477,0.0007688527,0.002571421],"study_design_scores_gemma":[0.0003750095,0.000106075,0.001370361,0.00006344416,0.00003824423,0.00002550999,0.00004579833,0.9778388,0.01951035,0.00006967494,0.000274344,0.0002823668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4548151,0.00001672437,0.5408574,0.003146941,0.0002286195,0.0002838013,0.00001365929,0.0003777882,0.0002600136],"genre_scores_gemma":[0.9942855,2.972353e-7,0.003874439,0.001424874,0.0001930986,0.00002992139,0.00007269543,0.00007272125,0.00004641672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5394704,"threshold_uncertainty_score":0.6854494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01146195500417232,"score_gpt":0.1860767770625264,"score_spread":0.1746148220583541,"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."}}