{"id":"W2555117105","doi":"10.1109/ijcnn.2016.7727697","title":"Ensemble Minimum Sum of Squared Similarities sampling for Nyström-based spectral clustering","year":2016,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"Genome British Columbia; Genome Canada","keywords":"Cluster analysis; Sampling (signal processing); Computer science; Spectral clustering; Rank (graph theory); Computational complexity theory; Selection (genetic algorithm); Pattern recognition (psychology); Artificial intelligence; Algorithm; Data mining; 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":[0.00006204456,0.0001290978,0.0001898905,0.00008227276,0.0000294735,0.00001585012,0.0001060887,0.00006722875,0.00003246608],"category_scores_gemma":[0.00002592912,0.0001005754,0.0000917501,0.0000456258,0.00003066037,0.00006691746,0.0000182368,0.00003489384,0.000001720184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003155968,"about_ca_system_score_gemma":0.00001230172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001484615,"about_ca_topic_score_gemma":0.00003656595,"domain_scores_codex":[0.9993352,0.000006770875,0.0002086791,0.0001256984,0.00008086111,0.0002428005],"domain_scores_gemma":[0.9994742,0.0001980093,0.0000275061,0.000210387,0.00005430693,0.00003562781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005062526,0.00001854415,0.000190174,0.0001222518,0.00003870466,0.000001511772,0.00006087509,0.006288394,0.9831489,0.0007368674,0.002900372,0.006442825],"study_design_scores_gemma":[0.00042138,0.00007347012,0.0001297016,0.0002020279,0.00001253676,0.000001451006,0.0000425015,0.05361969,0.9424577,0.001454254,0.001405021,0.0001802711],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1691503,0.00004676066,0.82722,0.00008533983,0.0001645093,0.000177477,0.00001633291,0.0006465011,0.002492795],"genre_scores_gemma":[0.9399918,0.000007733471,0.05971623,0.00003425109,0.00007627634,0.00001548472,0.00000278666,0.00003344606,0.0001220019],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7708415,"threshold_uncertainty_score":0.4101347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03815010857087771,"score_gpt":0.2574542387540497,"score_spread":0.219304130183172,"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."}}