{"id":"W2062892927","doi":"10.1109/icip.2012.6467209","title":"A focuss based method for low rank matrix recovery","year":2012,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inpainting; Solver; Matrix completion; Minification; Matrix (chemical analysis); Low-rank approximation; Computer science; Rank (graph theory); Mathematical optimization; Matrix norm; Norm (philosophy); Algorithm; Sparse matrix; Image (mathematics); Mathematics; Artificial intelligence; Combinatorics","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":[0.0006787889,0.0009861356,0.0006939612,0.0009828859,0.0004740676,0.0005873515,0.0011295,0.001278937,0.00492861],"category_scores_gemma":[0.001797468,0.0004408825,0.0007169791,0.0007059628,0.0006719186,0.001518067,0.001389327,0.001415398,0.001725021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003574295,"about_ca_system_score_gemma":0.0008492125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001070496,"about_ca_topic_score_gemma":0.002063295,"domain_scores_codex":[0.9993721,0.0001259839,0.00002773671,0.000103621,0.0003424398,0.00002823076],"domain_scores_gemma":[0.9994355,0.0001915329,0.00005372435,0.00009711982,0.0001867276,0.00003538036],"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":[0.000244799,0.0001489321,0.0005417152,0.0004581395,0.0001298563,0.0002484649,0.0003408942,0.07812271,0.1611588,0.04035101,0.01069469,0.7075599],"study_design_scores_gemma":[0.00004762047,0.0001389378,0.0002819304,0.00002408299,0.00002459398,0.0005193324,0.00004774762,0.9414093,0.03841412,0.006772101,0.01227995,0.00004030796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001600886,0.0001082439,0.9972319,0.00005465562,0.00002456299,0.00002771202,0.00002285194,0.0002749577,0.0006543015],"genre_scores_gemma":[0.04298475,0.0002846276,0.9525781,0.0001364224,0.0000764264,0.0001035502,0.0001979184,0.0001483292,0.003489801],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00492861,"threshold_uncertainty_score":0.0164879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01657521615217079,"score_gpt":0.2859125278382365,"score_spread":0.2693373116860657,"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."}}