{"id":"W2802552971","doi":"10.1109/tcss.2018.2813320","title":"A Distributed HOSVD Method With Its Incremental Computation for Big Data in Cyber-Physical-Social Systems","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Social Systems","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":125,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Big data; Computer science; Scalability; Computation; Dimensionality reduction; Tensor (intrinsic definition); Noise (video); Singular value decomposition; Cyber-physical system; Data mining; Artificial intelligence; Theoretical computer science; Distributed computing; Algorithm; Mathematics; Database","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":[0.0005993251,0.0006310531,0.0007172146,0.0005944026,0.0005122042,0.0009582324,0.001101887,0.000673869,0.001935379],"category_scores_gemma":[0.001800283,0.0003151799,0.0007264636,0.000920239,0.000674687,0.001316757,0.001044102,0.001124042,0.0004829089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004222675,"about_ca_system_score_gemma":0.001178532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004280966,"about_ca_topic_score_gemma":0.005472255,"domain_scores_codex":[0.9996722,0.00006991719,0.00002125404,0.00007447978,0.0001301768,0.00003214907],"domain_scores_gemma":[0.9993888,0.0002023192,0.00004654508,0.0001070953,0.0002129913,0.0000421113],"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":[0.0001791841,0.000112557,0.001655735,0.0002509398,0.0001069678,0.0002158688,0.0002336188,0.6012477,0.01558647,0.04675709,0.007143714,0.3265102],"study_design_scores_gemma":[0.000006783119,0.00001535304,0.00007969234,0.00000265207,0.000004053979,0.00001966346,0.00001292424,0.9939511,0.0009174343,0.00399818,0.0009857198,0.000006446624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004154233,0.000133665,0.994732,0.0001019806,0.00005938677,0.00002409268,0.00003179863,0.0001982362,0.0005646616],"genre_scores_gemma":[0.2176675,0.0004065315,0.7771475,0.0001363746,0.0001612252,0.0001511858,0.0003610949,0.0001337588,0.003834789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004280966,"threshold_uncertainty_score":0.00851208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1244148917783309,"score_gpt":0.3895373254667616,"score_spread":0.2651224336884307,"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."}}