Hyvärinen A, Hoyer P O, Inki M
Neural Comput. 2001 Jul;13(7):1527-58. doi: 10.1162/089976601750264992.
In ordinary independent component analysis, the components are assumed to be completely independent, and they do not necessarily have any meaningful order relationships. In practice, however, the estimated "independent" components are often not at all independent. We propose that this residual dependence structure could be used to define a topographic order for the components. In particular, a distance between two components could be defined using their higher-order correlations, and this distance could be used to create a topographic representation. Thus, we obtain a linear decomposition into approximately independent components, where the dependence of two components is approximated by the proximity of the components in the topographic representation.
在普通的独立成分分析中,假设各成分是完全独立的,并且它们不一定具有任何有意义的顺序关系。然而,在实际中,估计出的“独立”成分往往根本不是独立的。我们提出,可以利用这种残余的依赖结构来定义成分的拓扑顺序。具体而言,可以使用两个成分的高阶相关性来定义它们之间的距离,并且这个距离可以用于创建拓扑表示。因此,我们得到一种线性分解,分解为近似独立的成分,其中两个成分之间的依赖性通过它们在拓扑表示中的接近程度来近似。