说明：  独立成分分析（ICA）是最近开发的方法，它的目标是找到一种nongaussian等数据的线性表示 该元件是统计独立的或尽可能独立。应用包括信号特征提取与分离数据的基本结构。 在本文中，我们提出的基本理论和ICA的应用，以及我们对这个问题的近期工作。
(Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. In this paper, we present the basic theory and applications of ICA, and our recent work on the subject)