A relative trust-region algorithm for independent component analysis

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A relative trust-region algorithm for independent component analysis
In this paper we present a method of parameter optimization, relative trust-region learning, where the trust-region method and the relative optimization [21] 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 function (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. Key words: Blind source separation, Gradient-de...
Heeyoul Choi, Seungjin Choi
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2007
Where IJON
Authors Heeyoul Choi, Seungjin Choi
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