Big Data; data analysis; matrix algebra; principal component analysis; road traffic; abnormal data detection; anomaly-free case; missing data imputation; noisy observation traffic flow matrix; residual traffic matrix; robust PCA; robust principal component analysis; submatrices; traffic data analysis; traffic flow analysis reliability; traffic flow decomposition; traffic flow matrix analysis; traffic flow prediction; traffic matrix analysis; volume anomaly; Market research; Matrix decomposition; Predictive models; Principal component analysis; Road transportation; Robustness; Sparse matrices;
机译:具有连续流建模的张量鲁棒主成分分析:在大型交通网络异常交通模式提取中的应用
机译:基于小波包分解和核主成分分析的有效P2P流量识别方法
机译:基于在线块稳健的主成分分析分解的移动对象检测
机译:基于鲁棒主成分分析的流量分解和预测
机译:加速截断的奇异值分解:一种快速和可提供的鲁棒主成分分析方法
机译:交通崩溃严重性预测 - 混合主成分分析和机器学习模型的协同作用
机译:基于M估计的核主成分回归的鲁棒预测。