{"id":"W1963044959","doi":"10.1002/cem.2636","title":"Constrained kernelized partial least squares","year":2014,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Research Council Canada","keywords":"Partial least squares regression; Nonlinear system; Kernel (algebra); Latent variable; Noise (video); Mathematical optimization; Computer science; Variable (mathematics); Mathematics; Kernel method; Algorithm; Artificial intelligence; Machine learning; Support vector machine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001343877,0.001384145,0.001651335,0.0007727362,0.0004767221,0.001255686,0.00191273,0.001501103,0.004082729],"category_scores_gemma":[0.006413863,0.0006463318,0.001144235,0.001452151,0.0009796591,0.001821332,0.001935102,0.001686455,0.002329232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005355602,"about_ca_system_score_gemma":0.001811034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004045759,"about_ca_topic_score_gemma":0.00435769,"domain_scores_codex":[0.9980756,0.000615757,0.00008393247,0.0004787136,0.0006416087,0.0001043619],"domain_scores_gemma":[0.9978193,0.0009285675,0.0002480311,0.0004162969,0.0005244988,0.00006342572],"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":[0.0002256164,0.0001253028,0.001124018,0.0006207005,0.0003420031,0.0001753483,0.0001762616,0.4205434,0.02038983,0.03412775,0.009521656,0.5126282],"study_design_scores_gemma":[0.000009398712,0.00002174167,0.0004128362,0.00001436727,0.00001346692,0.00006581987,0.00001398478,0.9826401,0.003533053,0.009674701,0.003575554,0.00002487343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0023669,0.0002385037,0.9962524,0.00005882413,0.00002482224,0.00002559739,0.00009031617,0.0004192005,0.0005235521],"genre_scores_gemma":[0.1778834,0.0008375372,0.8104711,0.0002287093,0.0001103784,0.0002587393,0.001147255,0.0005362386,0.00852669],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004082729,"threshold_uncertainty_score":0.01365811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01519331425504999,"score_gpt":0.2429381963019769,"score_spread":0.2277448820469269,"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."}}