A relative trust-region algorithm for independent component analysis
SCIE
SCOPUS
- Title
- A relative trust-region algorithm for independent component analysis
- Authors
- Choi, H; Choi, S
- Date Issued
- 2007-03
- Publisher
- ELSEVIER SCIENCE BV
- Abstract
- 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 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. (c) 2006 Elsevier B.V. All rights reserved.
- Keywords
- blind source separation; gradient-descent learning; independent component analysis; relative optimization; trust-region methods; SOURCE SEPARATION
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/23463
- DOI
- 10.1016/j.neucom.2006.03.018
- ISSN
- 0925-2312
- Article Type
- Article
- Citation
- NEUROCOMPUTING, vol. 70, no. 40003, page. 1502 - 1510, 2007-03
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.