Performance Improvement of Independent Component Analysis by Fixed-point Algorithm of Adaptive Learning Parameters


The KIPS Transactions:PartB , Vol. 10, No. 4, pp. 397-402, Aug. 2003
10.3745/KIPSTB.2003.10.4.397,   PDF Download:

Abstract

This paper proposes an efficient fixed-point (FP) algorithm for improving performances of the independent component analysis (ICA) based on neural networks. The proposed algorithm is the FP algorithm based on Newton method for ICA using the adaptive learning parameters. The purpose of this algorithm is to improve the separation speed and performance by using the learning parameters in Newton method, which is based on the first order differential computation of entropy optimization function. The learning rate and the moment are adaptively adjusted according to an updating state of inverse mixing matrix. The proposed algorithm has been applied to the fingerprints and the images generated by random mixing matrix in the 8 fingerprints of 256x256-pixel and the 10 images of 512x512-pixel, respectively. The simulation results show that the proposed algorithm has the separation speed and performance better than those using the conventional FP algorithm based on Newton method. Especially, the proposed algorithm gives relatively larger improvement degree as the problem size increases.


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Cite this article
[IEEE Style]
J. Y. Hyeon and M. S. Jae, "Performance Improvement of Independent Component Analysis by Fixed-point Algorithm of Adaptive Learning Parameters," The KIPS Transactions:PartB , vol. 10, no. 4, pp. 397-402, 2003. DOI: 10.3745/KIPSTB.2003.10.4.397.

[ACM Style]
Jo Yong Hyeon and Min Seong Jae. 2003. Performance Improvement of Independent Component Analysis by Fixed-point Algorithm of Adaptive Learning Parameters. The KIPS Transactions:PartB , 10, 4, (2003), 397-402. DOI: 10.3745/KIPSTB.2003.10.4.397.