{"id":"W2158703881","doi":"","title":"Convex Multi-view Subspace Learning","year":2012,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Subspace topology; Representation (politics); Independence (probability theory); Computer science; Conditional independence; Regular polygon; Object (grammar); Curse of dimensionality; Convex optimization; Feature learning; Artificial intelligence; Pattern recognition (psychology); Algorithm; Mathematical optimization; Theoretical computer science; 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":[0.0009990551,0.001353923,0.001750203,0.0006338611,0.0004093545,0.001067367,0.001475475,0.001213047,0.002741407],"category_scores_gemma":[0.004115237,0.0005980546,0.0008036271,0.001379955,0.001019486,0.001727902,0.001775287,0.002052421,0.00138833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005933212,"about_ca_system_score_gemma":0.000786921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002394776,"about_ca_topic_score_gemma":0.002475383,"domain_scores_codex":[0.9989489,0.0004036462,0.00004429909,0.0002196858,0.00031967,0.00006389492],"domain_scores_gemma":[0.9985921,0.0006196765,0.0001285119,0.00032654,0.0002684012,0.00006472665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001467313,0.00009181337,0.0005715466,0.0002197482,0.0001157248,0.000142574,0.00009338756,0.6535103,0.008635802,0.05084626,0.01103453,0.2745915],"study_design_scores_gemma":[0.00000571684,0.00001957376,0.0000726527,0.000005384711,0.000003531783,0.0000449823,0.000008019171,0.9880477,0.00120893,0.009498934,0.001076954,0.000007551642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001306108,0.0001127394,0.9978001,0.00006456217,0.00001008068,0.00001467917,0.00004540541,0.0001463876,0.0004998547],"genre_scores_gemma":[0.1993541,0.000856161,0.7937003,0.00023443,0.0001414206,0.0002201317,0.001248966,0.0001862711,0.004058201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002741407,"threshold_uncertainty_score":0.00917089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02734920725914874,"score_gpt":0.2444688990466884,"score_spread":0.2171196917875397,"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."}}