Independent component analysis by general nonlinear Hebbian-like learning rules
Independent component analysis by general nonlinear Hebbian-like learning rules
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DOI：
10.1016/s0165-1684(97)00197-7
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发表时间：
1998-02-01
影响因子：
4.4
通讯作者：
Oja, E
中科院分区：
文献类型：
--
作者：
Hyvarinen, A;Oja, E
最近提出了一些用于独立分量分析（伊卡）的神经学习规则。这些规则通常是从信息理论的标准，如最大熵或最小互信息。在本文中，我们表明，事实上，伊卡可以通过非常简单的Hebbian或反Hebbian学习规则，这可能只有弱关系，这样的信息理论量。令人惊讶的是，实际上任何非线性函数都可以用于学习规则，只要正确选择赫布/反赫布项的符号。除了类似赫比的机制之外，这里的权重向量还被约束为具有单位范数，并且通过预白化或球化对数据进行预处理。这些结果意味着，人们可以选择的非线性，以优化所需的统计或数值标准。(C)1998 Elsevier Science B. V.保留所有权利。
A number of neural learning rules have been recently proposed for independent component analysis (ICA). The rules are usually derived from information-theoretic criteria such as maximum entropy or minimum mutual information. In this paper, we show that in fact, ICA can be performed by very simple Hebbian or anti-Hebbian learning rules, which may have only weak relations to such information-theoretical quantities. Rather surprisingly, practically any nonlinear function can be used in the learning rule, provided only that the sign of the Hebbian/anti-Hebbian term is chosen correctly. In addition to the Hebbian-like mechanism, the weight vector is here constrained to have unit norm, and the data is preprocessed by prewhitening, or sphering. These results imply that one can choose the non-linearity so as to optimize desired statistical or numerical criteria. (C) 1998 Elsevier Science B.V. All rights reserved.