An improved FastICA algorithm based on kurtosis index

doi:10.7523/j.issn.2095-6134.2019.03.015

Abstract

Abstract: Independent component analysis (ICA) is a popular signal processing method based on the high-order statistical characteristics of data. It has been widely used in imagery processing. However, classical FastICA algorithm achieves independent components (ICs) of the data by the fixed-point iteration method. It needs cyclic iterations to get every IC, and in the iterative process the solution of each IC requires the participation of all pixels. Therefore, when the amount of data or the number of pixels is large, FastICA is time-consuming. At this point, its speed advantage is greatly reduced. The remote sensing data generally has a large size. So how to directly apply FastICA to remote sensing data is a practical problem. In this study, by introducing the cokurtosis tensor, the fixed point iteration problem of FastICA is transformed into tensor computation in algebraic form, and the participation of all pixels is avoided, thus greatly reducing the computational complexity. Experimental results for multispectral images show that the proposed algorithm is faster than the classical FastICA algorithm based on kurtosis.

Key words: FastICA, high order statistics, kurtosis index, cokurtosis tensor

CLC Number:

TN911.7

MENG Lingbo, GENG Xiurui, YANG Weitun. An improved FastICA algorithm based on kurtosis index[J]. , 2019, 36(3): 410-416.

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