We study a multi-factor block model for variable clustering and connect it to the regularized subspace clustering by formulating a distributionally robust version of the nodewise regression. To solve the latter problem, we derive a convex relaxation, provide guidance on selecting the size of the robust region, and hence the regularization weighting parameter, based on the data, and propose an ADMM algorithm for implementation. We validate our method in an extensive simulation study. Finally, we propose and apply a variant of our method to stock return data, obtain interpretable clusters that facilitate portfolio selection and compare its out-of-sample performance with other clustering methods in an empirical study.

翻译：我们研究多要素组合组合的多要素区块模型,并将它与常规子空间组合联系起来,方法是开发一个分布稳健的节点回归版本。为了解决后一个问题,我们从中得出一个曲线松绑,根据数据为选择稳健区域的规模提供指导,从而提供正规化加权参数,并提议一个ADMM算法供实施。我们在一项广泛的模拟研究中验证了我们的方法。最后,我们提出并应用了我们的方法的变体来储存回报数据,获得可解释的组合,以便利组合选择,并在一项经验研究中将其外表外性性性能与其他组合方法进行比较。