{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001571595,0.001181177,0.001147486,0.0006958462,0.0003559568,0.0007899827,0.001101112,0.000859188,0.001328786],"category_scores_gemma":[0.004023009,0.0004587511,0.0009195944,0.0007641828,0.001306411,0.001132022,0.001933183,0.002338876,0.0003974817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007910648,"about_ca_system_score_gemma":0.0006591936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002058032,"about_ca_topic_score_gemma":0.001762551,"domain_scores_codex":[0.9990232,0.0004039119,0.00004179563,0.0002001007,0.0002495483,0.00008151565],"domain_scores_gemma":[0.9980872,0.001381947,0.0001500071,0.0001743607,0.0001529053,0.00005356823],"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":[0.00005102253,0.00002960706,0.0004923732,0.00005474874,0.00006488225,0.0000665043,0.00005483851,0.9309018,0.002153806,0.01977198,0.00144771,0.04491072],"study_design_scores_gemma":[0.000001711839,0.000009493902,0.00004914221,0.000003358908,0.000003652951,0.00001393892,0.000003308066,0.9933811,0.0004105105,0.005852838,0.0002668201,0.000004143736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005030962,0.0003103649,0.9935226,0.0001541421,0.00002674218,0.00002096769,0.00003586815,0.000139716,0.0007586],"genre_scores_gemma":[0.7099977,0.001134961,0.2814482,0.0004930534,0.0001671094,0.0003572089,0.0005489563,0.0001551835,0.005697683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002058032,"threshold_uncertainty_score":0.00831151,"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."}}