{"id":"W3155581945","doi":"10.1109/tii.2021.3074152","title":"Federated Tensor Decomposition-Based Feature Extraction Approach for Industrial IoT","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Foundation for Innovation","keywords":"Computer science; Dimension (graph theory); Data mining; Tensor (intrinsic definition); Decomposition; Feature extraction; Dimensionality reduction; Tensor decomposition; Big data; Data modeling; Machine learning; Artificial intelligence; Database; 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":[0.0008046278,0.0007753918,0.0008410232,0.0009299217,0.0004538467,0.0009443442,0.0008953887,0.0005180525,0.001458525],"category_scores_gemma":[0.001750777,0.0002969769,0.001091644,0.001437092,0.0006133339,0.001994519,0.001265602,0.0011776,0.0004435985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006800872,"about_ca_system_score_gemma":0.001097744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00367961,"about_ca_topic_score_gemma":0.00329326,"domain_scores_codex":[0.9993298,0.0001515281,0.00005243596,0.0001508449,0.0002245727,0.00009084294],"domain_scores_gemma":[0.9993444,0.0001275211,0.00009005843,0.00017977,0.0002152269,0.00004297605],"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":[0.0002772491,0.0001289578,0.002335175,0.000156209,0.0001370823,0.0003116124,0.0002514161,0.4813254,0.02092154,0.05337417,0.005412536,0.4353686],"study_design_scores_gemma":[0.00000556807,0.00002267801,0.0002585152,0.000005168964,0.00001031088,0.00005055164,0.00002755694,0.9800881,0.002442934,0.01579651,0.001281908,0.0000102103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005369744,0.0001046628,0.9936313,0.00007727102,0.00001697647,0.00001619187,0.00005325721,0.0003042399,0.0004262877],"genre_scores_gemma":[0.396762,0.0004433819,0.5987986,0.0001141022,0.00006854974,0.0001285827,0.0006698613,0.0001242683,0.002890641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00367961,"threshold_uncertainty_score":0.007316351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1128814839262955,"score_gpt":0.3448710913636853,"score_spread":0.2319896074373898,"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."}}