{"id":"W4401163230","doi":"10.1109/tits.2024.3430039","title":"Convolutional Low-Rank Tensor Representation for Structural Missing Traffic Data Imputation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Imputation (statistics); Representation (politics); Computer science; Missing data; Convolutional neural network; Tensor (intrinsic definition); Rank (graph theory); Artificial intelligence; Data mining; Pattern recognition (psychology); Natural language processing; Mathematics; Machine learning; Combinatorics; Pure mathematics","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":[0.002078664,0.001816082,0.00147302,0.0009737571,0.0005802944,0.001245942,0.001912351,0.0014386,0.002036423],"category_scores_gemma":[0.007386106,0.0007849617,0.001549376,0.001577099,0.001199838,0.002396713,0.001467683,0.003319043,0.001010742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001076918,"about_ca_system_score_gemma":0.002513777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008886685,"about_ca_topic_score_gemma":0.008659936,"domain_scores_codex":[0.9989979,0.0003210694,0.00006633507,0.0002484487,0.0002223635,0.0001439769],"domain_scores_gemma":[0.9970531,0.001178602,0.0004211461,0.0004690079,0.000702911,0.000175198],"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":[0.0002235775,0.0001029647,0.001910723,0.0003261476,0.000138026,0.0002355398,0.0001529785,0.822182,0.005989509,0.03407134,0.007795543,0.1268716],"study_design_scores_gemma":[0.000003543035,0.00001595863,0.00009540324,0.000006937375,0.00000638661,0.00001931682,0.000006881281,0.9927591,0.0006875837,0.005835389,0.0005536824,0.000009782623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005078798,0.0001739768,0.9936155,0.00016716,0.00003676942,0.0000170084,0.0001699165,0.0004329305,0.0003077946],"genre_scores_gemma":[0.3493187,0.001208925,0.6393774,0.0003915126,0.0002541184,0.00028691,0.003514914,0.0003999473,0.005247483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008886685,"threshold_uncertainty_score":0.01766992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1118770600711442,"score_gpt":0.3801490533734013,"score_spread":0.2682719933022571,"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."}}