{"id":"W3212438094","doi":"10.1109/tkde.2021.3126642","title":"Constrained Generative Adversarial Learning for Dimensionality Reduction","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionality reduction; Computer science; Artificial intelligence; Feature vector; Diffusion map; Big data; Pattern recognition (psychology); Data mining; Pairwise comparison; Projection (relational algebra); Curse of dimensionality; Reduction (mathematics); Transformation (genetics); Benchmark (surveying); Feature (linguistics); Machine learning; Nonlinear dimensionality reduction; Algorithm; Mathematics","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":[0.0002073729,0.0001385558,0.0001573198,0.00005863218,0.0002932984,0.00009648638,0.0001905987,0.00005682741,0.0000129727],"category_scores_gemma":[0.00003058778,0.0001423501,0.00004905892,0.0001955754,0.00002559444,0.0005372692,0.00001363803,0.0001574975,0.000004975591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002510235,"about_ca_system_score_gemma":0.00008530394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004134651,"about_ca_topic_score_gemma":0.000006910505,"domain_scores_codex":[0.9990528,0.0000567917,0.0001576229,0.0004800431,0.00007657201,0.0001761552],"domain_scores_gemma":[0.9991987,0.0001877791,0.00002724637,0.0003861118,0.0001217029,0.00007846877],"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":[0.00004190134,0.0002275879,0.000001545662,0.00006292642,0.0002473833,0.00001033301,0.0006987851,0.723832,0.1150334,0.003814332,0.001148844,0.154881],"study_design_scores_gemma":[0.0004574877,0.00004975749,0.0000104554,0.00002631374,0.00003040519,0.00002265248,0.00004502689,0.9134038,0.07856859,0.00005599288,0.007160215,0.0001692968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006033579,0.0002801181,0.9971722,0.0002045629,0.001384743,0.0001154932,0.00004982988,0.00009159432,0.0000981228],"genre_scores_gemma":[0.8817244,0.00008770729,0.1174958,0.0000188075,0.000294209,0.00002252623,0.00005335759,0.00001234533,0.0002908883],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.881121,"threshold_uncertainty_score":0.580487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02920903868555504,"score_gpt":0.2658531333269815,"score_spread":0.2366440946414265,"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."}}