{"id":"W1586572846","doi":"10.1109/ijcnn.2005.1556439","title":"Embedding via clustering: using spectral information to guide dimensionality reduction","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Dimensionality reduction; Maxima and minima; Cluster analysis; Spectral clustering; Eigendecomposition of a matrix; Computer science; Iterative method; Embedding; Eigenvalues and eigenvectors; Curse of dimensionality; Isomap; Reduction (mathematics); Artificial intelligence; Nonlinear dimensionality reduction; Algorithm; Pattern recognition (psychology); Data mining; 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":[0.001331945,0.001356671,0.001050438,0.002340639,0.001157671,0.001836613,0.001511534,0.001377504,0.002416458],"category_scores_gemma":[0.006183427,0.0007616713,0.0009064426,0.002461582,0.001302486,0.00327501,0.002527378,0.001586395,0.002010933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006142541,"about_ca_system_score_gemma":0.001116761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002296762,"about_ca_topic_score_gemma":0.003118723,"domain_scores_codex":[0.9988263,0.0003468535,0.00006623816,0.0002719315,0.000396405,0.00009225131],"domain_scores_gemma":[0.9980752,0.0006052734,0.0001976243,0.0005411556,0.0005078915,0.00007279948],"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":[0.000132777,0.0001842918,0.001598665,0.0002771144,0.0001344484,0.0001160111,0.0008791801,0.2449655,0.02529085,0.1018078,0.009375327,0.615238],"study_design_scores_gemma":[0.00001814687,0.00005187015,0.0003578273,0.00003902429,0.00002506844,0.00009911434,0.00009323023,0.8984851,0.01012114,0.0797531,0.01089699,0.00005941795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003264251,0.0001188808,0.9953938,0.00008904424,0.00002233266,0.00002737246,0.00003343551,0.0003692933,0.0006816535],"genre_scores_gemma":[0.06583433,0.0003037006,0.9312513,0.0001166047,0.00005508038,0.0001581497,0.0002553808,0.0003283131,0.00169719],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002416458,"threshold_uncertainty_score":0.00808388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04053276564319441,"score_gpt":0.2912275605935026,"score_spread":0.2506947949503082,"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."}}