{"id":"W4400128579","doi":"10.1007/978-981-97-0109-4_3","title":"Weighted Tensor Least Angle Regression for Solving Sparse Weighted Multilinear Least Squares Problems","year":2024,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Multilinear map; Tensor (intrinsic definition); Mathematics; Least-squares function approximation; Total least squares; Regression; Applied mathematics; Statistics; Pure mathematics","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":[0.0004464856,0.001207838,0.0005832182,0.0005004875,0.0002366335,0.0007131854,0.0008156594,0.0007181858,0.008975922],"category_scores_gemma":[0.001341115,0.0003253605,0.0004695017,0.001508202,0.0005230765,0.001143611,0.0007770446,0.001831986,0.004946601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002864952,"about_ca_system_score_gemma":0.0003719025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001259962,"about_ca_topic_score_gemma":0.002859169,"domain_scores_codex":[0.9996885,0.00008068437,0.00001393848,0.00004667881,0.0001585208,0.0000117316],"domain_scores_gemma":[0.9997347,0.0001295956,0.00001863681,0.00004175792,0.00006777792,0.000007519663],"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":[0.00004115938,0.00005688456,0.0001635885,0.0005639564,0.00006514276,0.00009976947,0.00008819676,0.137215,0.01685131,0.1930724,0.05147772,0.6003048],"study_design_scores_gemma":[0.000008825536,0.00005719458,0.0001925515,0.00008259861,0.00002589061,0.0001899157,0.00003250853,0.7916162,0.007801429,0.0897383,0.1102226,0.00003206627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000397893,0.0009923453,0.9926895,0.0001035887,0.0001787843,0.0000124284,0.00005950353,0.0002210226,0.005344884],"genre_scores_gemma":[0.0192281,0.004414888,0.9369595,0.0002373555,0.0004052015,0.0001278594,0.0004595591,0.0004694694,0.0376982],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008975922,"threshold_uncertainty_score":0.03002745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03067183584834947,"score_gpt":0.2374033422004046,"score_spread":0.2067315063520552,"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."}}