In independent component analysis (ICA) it is often assumed that the p components of the observation vector are linear combinations of p underlying independent components. Two scatter matrices having the so called independence property can then be used to recover the independent components. The assumption of (exactly) p independent components is however often criticized, and several alternative and more realistic models have been suggested. One of these models is the independent subspace model where it is assumed that the p-variate observed vectors are based on k independent subvectors of lengths p, ..., p, p+...+p = p. In independent subspace analysis (ISA) the aim is to recover these subvectors. In this paper we describe a solution to ISA which is based on the use of three scatter matrices with the independent block property.
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机译：在独立成分分析（ICA）中，通常假定观察向量的p个成分是p个基础独立成分的线性组合。然后可以使用具有所谓的独立性的两个散射矩阵来恢复独立分量。然而，经常会批评（完全）p个独立成分的假设，并提出了几种替代的和更现实的模型。这些模型之一是独立子空间模型，其中假定p变量观测向量基于长度为p，...，p，p + ... + p = p的k个独立子向量。在独立子空间分析（ISA）中，目标是恢复这些子向量。在本文中，我们描述了基于三个具有独立块属性的散布矩阵的ISA解决方案。
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