A "nonnegative PCA" algorithm for independent component analysis

被引：75

作者：

Plumbley, MD

Oja, E

机构：

[1] Queen Mary Univ London, Dept Elect Engn, London E1 4NS, England

[2] Aalto Univ, Lab Comp & Informat Sci, Helsinki 02015, Finland

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2004年 / 15卷 / 01期

基金：

芬兰科学院; 英国工程与自然科学研究理事会;

关键词：

independent component analysis (ICA); nonlinear principal component analysis (nonlinear PCA); nonnegative matrix factorization; subspace learning rule;

D O I：

10.1109/TNN.2003.820672

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We consider the task of independent component analysis when the independent sources are known to be nonnegative and well-grounded, so that they have a nonzero probability density function (pdf) in the region of zero. We propose the use of a "nonnegative principal component analysis (nonnegative PCA)" algorithm, which is a special case of the nonlinear PCA algorithm, but with a rectification nonlinearity, and we conjecture that this algorithm will find such nonnegative well-grounded independent sources, under reasonable initial conditions. While the algorithm has proved difficult to analyze in the general case, we give some analytical results that are consistent with this conjecture and some numerical simulations that illustrate its operation.

引用

页码：66 / 76

页数：11

共 29 条

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