{"id":"W2950516059","doi":"10.48550/arxiv.1902.09938","title":"A Feature Selection Based on Perturbation Theory","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Row; Singular value decomposition; Feature selection; Row and column spaces; Perturbation (astronomy); Singular value; Selection (genetic algorithm); Pattern recognition (psychology); Mathematics; Algorithm; Computer science; Artificial intelligence; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002137465,0.0002258687,0.0001756143,0.0002921521,0.0001273688,0.0001139889,0.0006816821,0.0003798939,0.00005810667],"category_scores_gemma":[0.00003615126,0.0002302832,0.0001592013,0.0003834775,0.00002363614,0.0002921792,0.0003112631,0.0005879216,0.000344196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001685644,"about_ca_system_score_gemma":0.0001562917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007292854,"about_ca_topic_score_gemma":0.000002658168,"domain_scores_codex":[0.998554,0.0002321772,0.00007428397,0.0008329733,0.0001047067,0.0002018686],"domain_scores_gemma":[0.9988162,0.0001481469,0.0001571081,0.0006624364,0.0001395346,0.00007659647],"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":[0.0001851381,0.0001609811,0.0006705521,0.0001049275,0.00004234025,0.0000399437,0.0001559488,0.853008,0.00020494,0.1341062,0.008281739,0.003039347],"study_design_scores_gemma":[0.000386243,0.00008794685,0.0004486181,0.0001765631,0.0000257386,0.000001139028,0.00002077172,0.9417772,0.0006916348,0.05481525,0.001280779,0.0002881368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03734444,0.00001544083,0.949685,0.0005009585,0.0008431868,0.0003352776,0.000009436267,0.0003205184,0.0109458],"genre_scores_gemma":[0.9832309,0.00002402492,0.00129034,0.0005877413,0.00006739252,0.000001474459,0.00004813919,0.00001283662,0.01473718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9483946,"threshold_uncertainty_score":0.9390677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03825446788731093,"score_gpt":0.1724732841448097,"score_spread":0.1342188162574987,"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."}}