Recently, several algorithms have been proposed for independent subspace analysis where hidden variables are i.i.d. processes. We show that these methods can be extended to certain AR, MA, ARMA and ARIMA tasks. Central to our paper is that we introduce a cascade of algorithms, which aims to solve these tasks without previous knowledge about the number and the dimensions of the hidden processes. Our claim is supported by numerical simulations. As a particular application, we search for subspaces of facial components.

翻译：最近,为独立的子空间分析提出了几种算法,其中隐藏变量是过程。我们显示,这些方法可以扩展到某些AR、MA、ARMA和ARIMA任务。我们论文的核心是,我们采用一系列算法,目的是在没有事先了解隐藏过程的数量和维度的情况下完成这些任务。我们的要求得到了数字模拟的支持。作为一个特定的应用,我们搜索面部组成部分的子空间。