{"id":"W4287169430","doi":"10.48550/arxiv.2105.12005","title":"Hierarchical Subspace Learning for Dimensionality Reduction to Improve\\n Classification Accuracy in Large Data Sets","year":2021,"lang":"","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":"University of Waterloo","funders":"","keywords":"Dimensionality reduction; Linear discriminant analysis; Subspace topology; Principal component analysis; Pattern recognition (psychology); Artificial intelligence; Nonlinear dimensionality reduction; Projection (relational algebra); Mathematics; Computer science; Random subspace method; Curse of dimensionality; Machine learning; Algorithm","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":[0.002172014,0.0009262172,0.0009539194,0.002004938,0.0007953463,0.000940688,0.0008510583,0.0005507303,0.001991673],"category_scores_gemma":[0.006582459,0.0002110818,0.001027027,0.0025456,0.000693425,0.001408976,0.001400198,0.001206755,0.001166074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006686049,"about_ca_system_score_gemma":0.001323575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004445598,"about_ca_topic_score_gemma":0.007483081,"domain_scores_codex":[0.9974458,0.0008709505,0.000162583,0.0004021366,0.0009381056,0.0001803734],"domain_scores_gemma":[0.9978756,0.0007195945,0.0001365509,0.0006425841,0.0005579168,0.00006773814],"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":[0.0001277835,0.0002751634,0.00356942,0.000172985,0.0001739233,0.00008092637,0.0002549342,0.07907375,0.02471113,0.01357891,0.009476928,0.868504],"study_design_scores_gemma":[0.00001458784,0.0001146493,0.002304601,0.0000192429,0.00002839597,0.00007322798,0.00008726568,0.9619573,0.01837397,0.01307761,0.003920146,0.00002898255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02657767,0.0004498586,0.9692417,0.0002140421,0.00006087641,0.00009383771,0.0001924158,0.002091601,0.001077947],"genre_scores_gemma":[0.2734673,0.0003741949,0.7227516,0.0001319137,0.00006789025,0.0002345724,0.001094121,0.0001944422,0.001684018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004445598,"threshold_uncertainty_score":0.01148683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1609891884561507,"score_gpt":0.2695604746280671,"score_spread":0.1085712861719164,"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."}}