{"id":"W2189326083","doi":"10.1007/978-3-319-40643-5_2","title":"On the PLS Algorithm for Multiple Regression (PLS1)","year":2016,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematics; Conjugate gradient method; Curse of dimensionality; Partial least squares regression; Algorithm; Estimator; Lanczos resampling; Krylov subspace; Applied mathematics; Mathematical optimization; Statistics; Iterative method; Eigenvalues and eigenvectors","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00315812,0.003368874,0.001399633,0.001343314,0.0007517654,0.002102762,0.002100406,0.001883456,0.009439054],"category_scores_gemma":[0.006369085,0.001141671,0.002154696,0.003662398,0.001502459,0.002648507,0.003025045,0.005428171,0.01007224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005258179,"about_ca_system_score_gemma":0.0009091846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002130741,"about_ca_topic_score_gemma":0.002020309,"domain_scores_codex":[0.9974837,0.001161844,0.00009146659,0.000407903,0.0007713444,0.00008377877],"domain_scores_gemma":[0.9983404,0.0009914495,0.0000535334,0.0002937788,0.0002945879,0.00002623571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000101504,0.00009491471,0.0003008712,0.0004771144,0.0001671559,0.0001376496,0.0001576283,0.08814581,0.006790068,0.08273705,0.04537463,0.7755156],"study_design_scores_gemma":[0.00003707605,0.00008777947,0.0006094678,0.0001278475,0.00007693458,0.0003215925,0.00004188848,0.5778159,0.0101506,0.2966055,0.1139923,0.0001331549],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003958606,0.001400351,0.9954336,0.0001516813,0.0001634357,0.0000177712,0.00009045623,0.0009313375,0.001415543],"genre_scores_gemma":[0.01638696,0.00468173,0.9674192,0.00040857,0.0005391964,0.0001608646,0.0008881452,0.001420616,0.008094748],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009439054,"threshold_uncertainty_score":0.03157675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02826364409263911,"score_gpt":0.2796860828668493,"score_spread":0.2514224387742102,"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."}}