{"id":"W2562813894","doi":"10.1088/1361-6420/aa5489","title":"The pre-image problem for Laplacian Eigenmaps utilizing <i>L</i> <sub>1</sub> regularization with applications to data fusion","year":2016,"lang":"en","type":"article","venue":"Inverse Problems","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Defense Threat Reduction Agency; University of Southern Mississippi; Ryerson University; U.S. Department of Energy","keywords":"Artificial intelligence; Mathematics; Laplace operator; Nonlinear dimensionality reduction; Feature (linguistics); Computer vision; Pattern recognition (psychology); Inpainting; Image (mathematics); Computer science; Dimensionality reduction","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001753228,0.00117595,0.0008285437,0.0008864297,0.000620831,0.001418981,0.001207438,0.001685954,0.002877658],"category_scores_gemma":[0.005367777,0.0006854139,0.001023737,0.0008894877,0.002145577,0.0020798,0.001639989,0.002980199,0.001179934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007171237,"about_ca_system_score_gemma":0.001459381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001974992,"about_ca_topic_score_gemma":0.002368377,"domain_scores_codex":[0.999305,0.0001880901,0.0000409592,0.0001452439,0.000266688,0.00005395016],"domain_scores_gemma":[0.9981085,0.00100137,0.00018244,0.0002867103,0.0003566015,0.00006445612],"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":[0.0001596492,0.0001171631,0.0009562891,0.0005174274,0.00009035508,0.0002375171,0.0004842093,0.281078,0.04535747,0.1328605,0.008752806,0.5293886],"study_design_scores_gemma":[0.00001052621,0.00004730831,0.0003080406,0.00002161793,0.00001031463,0.0001646316,0.00005850281,0.9464935,0.01049633,0.03845481,0.003904462,0.00002990155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001675268,0.00004906661,0.997577,0.0001235714,0.00001444378,0.00001848562,0.00001228822,0.0001320073,0.0003979315],"genre_scores_gemma":[0.0533307,0.0002242221,0.9435933,0.0001084376,0.00006089611,0.0001254383,0.0001159652,0.0002098222,0.002231122],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002877658,"threshold_uncertainty_score":0.009626687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01853556008918288,"score_gpt":0.2449558482138186,"score_spread":0.2264202881246357,"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."}}