论文标题
具有复杂组结构的高维多响应模型中稀疏恢复的顺序逐步筛选程序
A sequential stepwise screening procedure for sparse recovery in high-dimensional multiresponse models with complex group structures
论文作者
论文摘要
在许多领域都出现了带有复杂组结构的多源数据，但由于难以识别复杂的组结构，因此仅研究了一些方法。我们提出了一种称为顺序逐步筛选程序（SES）的新型算法，用于具有复杂组结构的高维多响应模型中的特征选择。该算法鼓励分组效应，在响应和预测因子来自不同组的情况下，允许每个响应组与多个预测指标组相关。为了在复杂组结构下获得正确的模型，提出的程序首先通过规范相关度量（CC）选择非零块和非零行，然后通过扩展的贝叶斯信息标准（EBIC）选择非零条目。我们表明，这种方法在极稀疏的模型中是准确的，并且在计算上很有吸引力。建立了SES的理论特性。我们进行仿真研究，并考虑一个真实的例子，将其性能与现有方法进行比较。
Multiresponse data with complex group structures in both responses and predictors arises in many fields, yet, due to the difficulty in identifying complex group structures, only a few methods have been studied on this problem. We propose a novel algorithm called sequential stepwise screening procedure (SeSS) for feature selection in high-dimensional multiresponse models with complex group structures. This algorithm encourages the grouping effect, where responses and predictors come from different groups, further, each response group is allowed to relate to multiple predictor groups. To obtain a correct model under the complex group structures, the proposed procedure first chooses the nonzero block and the nonzero row by the canonical correlation measure (CC) and then selects the nonzero entries by the extended Bayesian Information Criterion (EBIC). We show that this method is accurate in extremely sparse models and computationally attractive. The theoretical property of SeSS is established. We conduct simulation studies and consider a real example to compare its performances with existing methods.
