{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001452288,0.001811355,0.001417034,0.001499824,0.0007736348,0.001380378,0.001646612,0.001462187,0.002802012],"category_scores_gemma":[0.005294221,0.000559608,0.0009996712,0.001983935,0.001081458,0.001967546,0.001615881,0.002298904,0.003779821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008963174,"about_ca_system_score_gemma":0.001269295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003435876,"about_ca_topic_score_gemma":0.004125854,"domain_scores_codex":[0.9977748,0.0006568384,0.0001086482,0.000608703,0.0007147805,0.0001362967],"domain_scores_gemma":[0.9982862,0.0004424596,0.0002050126,0.0004487639,0.0005592691,0.00005820052],"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":[0.0001680082,0.0001097086,0.0004824497,0.0002880199,0.0001243514,0.00009480421,0.0002066085,0.4810304,0.01824484,0.03572703,0.01338358,0.4501402],"study_design_scores_gemma":[0.000004214278,0.00003876716,0.0001406731,0.000008775356,0.000007311457,0.00003931926,0.0000235881,0.9806892,0.003669573,0.01312589,0.002231018,0.00002168318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002026177,0.0001998944,0.9963644,0.00008082219,0.00002568898,0.00001951481,0.00006981968,0.0007166779,0.0004969661],"genre_scores_gemma":[0.1675607,0.0006259572,0.8239546,0.0002125374,0.0001654662,0.0002577586,0.001476374,0.0005253333,0.00522128],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003435876,"threshold_uncertainty_score":0.009373665,"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."}}