{"id":"W2152116910","doi":"","title":"Rank-One Matrix Pursuit for Matrix Completion","year":2014,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Matrix completion; Eight-point algorithm; Singular value decomposition; Low-rank approximation; Computer science; Algorithm; Matrix (chemical analysis); Scalability; Rank (graph theory); Matching pursuit; Convergence (economics); Sparse matrix; Matrix decomposition; Mathematical optimization; State-transition matrix; Mathematics; Symmetric matrix; Compressed sensing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001081291,0.00009723796,0.0001466321,0.00005641582,0.00004014249,0.00003951431,0.000113693,0.00005849408,0.00007497153],"category_scores_gemma":[0.00001476309,0.00009623562,0.00005690095,0.00005359473,0.000012628,0.000049747,0.00001765364,0.00005226499,0.0000619769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000173524,"about_ca_system_score_gemma":0.000001964069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001322767,"about_ca_topic_score_gemma":0.000004936194,"domain_scores_codex":[0.9994919,0.000009970312,0.0001288822,0.0001047124,0.00009397008,0.0001705663],"domain_scores_gemma":[0.9996549,0.00005813947,0.00001540056,0.0001946852,0.00004162481,0.00003526637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006818862,0.00007812338,0.0001925493,0.0001944342,0.0001396559,0.000001769431,0.0001035856,0.01328008,0.4794057,0.1648287,0.2987481,0.04295922],"study_design_scores_gemma":[0.0007676099,0.0001167914,0.0006532645,0.00006101191,0.00004465213,0.00000922579,0.00001348013,0.543344,0.1736326,0.02070748,0.260226,0.0004239272],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0214886,0.0001170344,0.9616983,0.0001897872,0.0002087716,0.0002646345,0.000004200128,0.001934206,0.01409448],"genre_scores_gemma":[0.9400682,0.00001705608,0.05894635,0.00007266046,0.0001940204,0.00002277992,0.00001550765,0.00003228806,0.0006311738],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9185796,"threshold_uncertainty_score":0.3924375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01993682330091582,"score_gpt":0.2622091324008272,"score_spread":0.2422723090999113,"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."}}