{"id":"W2009727312","doi":"10.1016/j.neunet.2007.04.018","title":"Nonlinear principal component analysis of noisy data","year":2007,"lang":"en","type":"article","venue":"Neural Networks","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":43,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Freie Universität Berlin","keywords":"Overfitting; Principal component analysis; Nonlinear system; Regularization (linguistics); Artificial neural network; Artificial intelligence; Computer science; Component (thermodynamics); Pattern recognition (psychology); Mathematics; Algorithm","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002299326,0.0001544119,0.0004247379,0.0002156517,0.00005578735,0.00001703544,0.0006656226,0.0001314442,0.001091177],"category_scores_gemma":[0.00004625109,0.0001385902,0.0001684591,0.001770803,0.00007673002,0.00007483063,0.0002684983,0.0002759591,0.000003369368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003154393,"about_ca_system_score_gemma":0.000009016859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001141493,"about_ca_topic_score_gemma":0.0001339192,"domain_scores_codex":[0.9985889,0.000008665922,0.0004195311,0.0003629723,0.0002706966,0.0003493008],"domain_scores_gemma":[0.9984757,0.0002077852,0.0001918371,0.0009579429,0.0000534973,0.000113203],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007348496,0.001753615,0.7399837,0.0001895055,0.0120339,0.000217037,0.0001936141,0.1439988,0.07277013,0.0002772129,0.003882848,0.02396488],"study_design_scores_gemma":[0.0002994411,0.00002168365,0.01334177,0.000005305359,0.002931142,0.000003974381,0.00007155183,0.9585782,0.022414,0.000003226993,0.002110741,0.0002188991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865104,0.0008004758,0.007609297,0.00004850072,0.00007937379,0.00002944369,0.00006837056,0.00006627124,0.004787893],"genre_scores_gemma":[0.9976592,0.00005140415,0.0008039887,0.0001033493,0.0003319832,7.13482e-7,0.0007291987,0.00001387452,0.0003062781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8145795,"threshold_uncertainty_score":0.999822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03856100644950894,"score_gpt":0.32030128146221,"score_spread":0.281740275012701,"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."}}