A relative trust-region algorithm for independent component analysis

作者：Choi Heeyoul; Choi Seungjin^*

来源：Neurocomputing, 2007, 70(7-9): 1502-1510.

DOI：10.1016/j.neucom.2006.03.018

• blind source separation
• gradient-descent learning
• independent component analysis
• relative optimization
• trust-region methods

In this paper we present a method of parameter optimization, relative trust-region learning, where the trust-region method and the relative optimization [M. Zibulevsky, Blind source separation with relative Newton method, in: Proceedings of the ICA, Nara, Japan, 2003, pp. 897-902] are jointly exploited. The relative trust-region method finds a direction and a step size with the help of a quadratic model of the objective as in the conventional trust-region methods) and updates parameters in a multiplicative fashion (as in the relative optimization). We apply this relative trust-region learning method to the problem of independent component analysis (ICA), which leads to the relative TR-ICA algorithm which turns out to possess the equivariant property (as in the relative gradient) and to achieve faster convergence than the relative gradient and even Newton-type algorithms. Empirical comparisons with several existing ICA algorithms demonstrate the useful behavior of the relative TR-ICA algorithm, such as the equivariant property and fast convergence.

• 出版日期2007-3