{"id":"W2979163681","doi":"10.3233/ida-194486","title":"A comparison study on nonlinear dimension reduction methods with kernel variations: Visualization, optimization and classification","year":2020,"lang":"en","type":"preprint","venue":"Intelligent Data Analysis","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Dimensionality reduction; Pattern recognition (psychology); Principal component analysis; Linear discriminant analysis; Computer science; Support vector machine; Kernel principal component analysis; Kernel (algebra); Benchmark (surveying); Feature extraction; Local binary patterns; Dimension (graph theory); Machine learning; Kernel method; Histogram; Mathematics; Image (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":[0.002862042,0.001037425,0.001261028,0.001973212,0.0004018668,0.001892745,0.0008266966,0.0008854179,0.001740873],"category_scores_gemma":[0.01029424,0.0003452418,0.00110786,0.002150255,0.0006905293,0.002190863,0.001075324,0.001058228,0.0005619713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006902309,"about_ca_system_score_gemma":0.0008223362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003339036,"about_ca_topic_score_gemma":0.001777433,"domain_scores_codex":[0.998174,0.0007289654,0.00014711,0.0002955729,0.0005619383,0.00009246374],"domain_scores_gemma":[0.9946549,0.002844298,0.0002966688,0.0007777027,0.001299423,0.0001270112],"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":[0.0005883278,0.0003229657,0.0033974,0.0006888522,0.0002419425,0.00007650672,0.0002426672,0.1159791,0.006880614,0.01463714,0.005381838,0.8515626],"study_design_scores_gemma":[0.00002791075,0.0002661897,0.003372864,0.00006123274,0.00005717277,0.0001631682,0.0000894269,0.9812506,0.004620295,0.0048678,0.00516935,0.00005394857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1060097,0.02677217,0.8564581,0.001383518,0.0004409014,0.0002219414,0.0002352168,0.001621108,0.006857285],"genre_scores_gemma":[0.4900041,0.01382086,0.4896221,0.0001974116,0.0003294819,0.0002641235,0.000734003,0.0005377032,0.004490197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003339036,"threshold_uncertainty_score":0.01513606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1920095745022328,"score_gpt":0.4478469634700107,"score_spread":0.2558373889677779,"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."}}