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http://dx.doi.org/10.3745/KIPSTB.2002.9B.5.643

Independent Component Analysis Based on Neural Networks Using Hybrid Fixed-Point Algorithm  

Cho, Yong-Hyun (대구가톨릭대학교 컴퓨터정보통신공학부)
Abstract
This paper proposes an efficient hybrid 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 secant method and momentum for ICA. Secant method is applied to improve the separation performance by simplifying the computation process for estimating the root of objective function, which is to minimize the mutual informations of the independent components. The momentum is applied for high-speed convergence by restraining the oscillation if the process of converging to the optimal solution. It can simultaneously achieve a superior properties of the secant method and the momentum. The proposed algorithm has been applied to the composite fingerprints and the images generated by random mixing matrix in the 8 fingerprints of $256\times{256}$-pixel and the 10 images of $512\times{512}$-pixel, respectively. The simulation results show that the proposed algorithm has better performances of the separation speed and rate than those using the FP algorithm based on Newton and secant method. Especially, the secant FP algorithm can be solved the separating performances depending on initial points settings and the nonrealistic learning time for separating the large size images by using the Newton FP algorithm.
Keywords
Neural Networks; Principal Component Analysis; Independent Component Analysis; Newton; Secant; Momentum;
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Times Cited By KSCI : 2  (Citation Analysis)
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