{"id":"W2401963278","doi":"","title":"A novel greedy algorithm for Nyström approximation","year":2011,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Greedy algorithm; Algorithm; Approximation algorithm; Kernel (algebra); Mathematical optimization; Benchmark (surveying); Sampling (signal processing); Greedy randomized adaptive search procedure; Mathematics; Matrix (chemical analysis); Computer science; Combinatorics; Filter (signal processing)","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":[0.0008584122,0.00115374,0.001490806,0.001054944,0.0006247304,0.001073616,0.001730685,0.001334333,0.003082814],"category_scores_gemma":[0.003708518,0.0005800204,0.0007956397,0.001522688,0.000733178,0.001314718,0.001477215,0.001347419,0.001752699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006785529,"about_ca_system_score_gemma":0.001755078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003265799,"about_ca_topic_score_gemma":0.004132616,"domain_scores_codex":[0.9989895,0.0002411792,0.00006205044,0.0001728497,0.0004297962,0.000104605],"domain_scores_gemma":[0.999195,0.0003855641,0.00006489966,0.0001120549,0.0002021076,0.00004031952],"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":[0.0002528574,0.0001185586,0.000785113,0.0002061178,0.0001098312,0.0001514599,0.0001318263,0.3661624,0.01639698,0.0488753,0.01360173,0.5532079],"study_design_scores_gemma":[0.00003353301,0.00004170243,0.00009356929,0.00001131265,0.000008801516,0.00009608263,0.00001797188,0.9815587,0.002398758,0.01103718,0.004686784,0.00001551541],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001219474,0.0001043313,0.9977908,0.00004778356,0.00004026708,0.00003342154,0.00003299483,0.0002694461,0.0004615577],"genre_scores_gemma":[0.04250418,0.0002563047,0.9540479,0.0001420115,0.00008425586,0.0002938606,0.0003743732,0.0001274687,0.00216967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003265799,"threshold_uncertainty_score":0.01031303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05082590369836738,"score_gpt":0.2250647369847518,"score_spread":0.1742388332863844,"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."}}