{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002087596,0.0008448581,0.001180495,0.0009054155,0.0007264391,0.0009615885,0.001300285,0.001206387,0.00147971],"category_scores_gemma":[0.009523519,0.0003965642,0.0006991189,0.00119872,0.0009003772,0.001626155,0.001488765,0.001412032,0.0006168931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007454491,"about_ca_system_score_gemma":0.001182409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001857006,"about_ca_topic_score_gemma":0.002531833,"domain_scores_codex":[0.9983811,0.0006471425,0.0000820416,0.0002380893,0.0005837806,0.00006787595],"domain_scores_gemma":[0.9971777,0.001521805,0.0002560755,0.0004220164,0.0005256037,0.00009673146],"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":[0.0003194094,0.0001299759,0.001919729,0.000252737,0.0001315277,0.0001303001,0.000261256,0.6557472,0.01428688,0.07989302,0.003445662,0.2434823],"study_design_scores_gemma":[0.000007136175,0.00003056765,0.0001733092,0.000007229685,0.00000461436,0.00004335355,0.00001634666,0.9870175,0.002025878,0.00971638,0.0009471395,0.00001066186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004974929,0.0001059878,0.9943438,0.00005835652,0.00001752197,0.00002278146,0.00002408594,0.00009902318,0.0003536207],"genre_scores_gemma":[0.2163061,0.0002971922,0.7810757,0.0001200787,0.00009275625,0.000228364,0.0003430507,0.00009687463,0.001439938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002087596,"threshold_uncertainty_score":0.01104039,"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."}}