Fast and Scalable Cellwise-Robust Ensembles for High-Dimensional Data
面向高维数据的快速可扩展逐单元稳健集成方法
Variable selection and ensemble methods are central to high-dimensional modelling, enabling the identification of relevant predictors and the construction of stable predictive signals through aggregation across multiple models. However, in practice, high-dimensional data are often affected by cellwise contamination, in which individual cells of the data matrix deviate from the underlying multivariate structure without necessarily making the corresponding observation…