{"id":"W4210647131","doi":"10.1109/tkde.2022.3144294","title":"Low-Rank Linear Embedding for Robust Clustering","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; Natural Science Foundation of Shenzhen City","keywords":"Cluster analysis; Dimensionality reduction; Computer science; Embedding; Robustness (evolution); Correlation clustering; Curse of dimensionality; Artificial intelligence; Pattern recognition (psychology); Algorithm","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":[0.0002005812,0.0001224573,0.000120961,0.000165797,0.0003908662,0.00006476248,0.0005113992,0.00003146274,0.00002688305],"category_scores_gemma":[0.000005006876,0.0001333522,0.00003875982,0.0002128481,0.000006625678,0.0005273249,0.00004056475,0.0001990688,0.00001194505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003047033,"about_ca_system_score_gemma":0.00002297823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003347445,"about_ca_topic_score_gemma":0.00000517974,"domain_scores_codex":[0.9991416,0.00001625883,0.0001525234,0.0003843992,0.00009920085,0.0002060682],"domain_scores_gemma":[0.999265,0.0001267844,0.00002205148,0.0004881177,0.00002424847,0.00007379521],"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":[0.00001882157,0.0001471689,4.603012e-7,0.0001293334,0.00002736948,0.00000368678,0.0005372553,0.9078336,0.004695397,0.00004427224,0.001055859,0.08550673],"study_design_scores_gemma":[0.0003677404,0.00007624173,0.000001686911,0.0000478769,0.00001101199,0.00001631822,0.00004649566,0.9828688,0.005675514,0.000007355616,0.01071663,0.0001643403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001852922,0.0001284653,0.9962308,0.00006975468,0.001120644,0.0001678713,0.0001727822,0.0002021823,0.00005451018],"genre_scores_gemma":[0.8267753,0.0001112967,0.1719919,0.00009980482,0.0001826769,0.0002946222,0.0000934653,0.00004626918,0.0004046505],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8249224,"threshold_uncertainty_score":0.5437945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03821421735241483,"score_gpt":0.2793082329249661,"score_spread":0.2410940155725513,"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."}}