{"id":"W2812241481","doi":"10.3390/e20070519","title":"Projected Affinity Values for Nyström Spectral Clustering","year":2018,"lang":"en","type":"article","venue":"Entropy","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Eigenvalues and eigenvectors; Mathematics; Cluster analysis; Projection (relational algebra); Kernel (algebra); Kernel method; Gaussian function; Gaussian; Similarity (geometry); Quadratic equation; Point (geometry); Support vector machine; Applied mathematics; Combinatorics; Algorithm; Artificial intelligence; Computer science; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009555435,0.0000724473,0.00007598852,0.00004403463,0.0001407206,0.00009687644,0.0002673595,0.00003469293,0.00005227185],"category_scores_gemma":[0.00004742154,0.00006138093,0.00004431606,0.0001096432,0.0000280628,0.0002502136,0.00008935071,0.00004660759,0.0001559705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001847074,"about_ca_system_score_gemma":0.00002526463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001038434,"about_ca_topic_score_gemma":0.000006870304,"domain_scores_codex":[0.999337,0.00002619459,0.0001022037,0.0002110031,0.0001077606,0.0002158901],"domain_scores_gemma":[0.9996029,0.00003651846,0.0000402553,0.000202566,0.00007081332,0.00004694165],"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":[0.0004958471,0.000710807,0.002415555,0.0002027154,0.0001409468,0.00002325385,0.01113294,0.00008546789,0.5493075,0.06013684,0.234168,0.1411801],"study_design_scores_gemma":[0.001968866,0.0009872367,0.004657343,0.0001157206,0.00001577201,0.00001702441,0.0001443576,0.2581433,0.6788601,0.02698792,0.02758084,0.0005214986],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2330575,0.00001081351,0.7632067,0.0005382037,0.0008467686,0.0002679177,0.000004307702,0.0002531036,0.001814656],"genre_scores_gemma":[0.8401732,0.000003779009,0.1584323,0.0002596423,0.0006143113,0.00003640524,0.000006079837,0.000007682283,0.0004665933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6071157,"threshold_uncertainty_score":0.2503042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02486172068981743,"score_gpt":0.278469608701322,"score_spread":0.2536078880115045,"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."}}