A Parallel Independent Component Implement Based on Learning Updating with Forms of Matrix Transformations

Authors:
Jing-Hui Wang;Guang-Qian Kong;Cai-Hong Liu
Affiliations:
TianJin University of Technology, TianJin, 300191, China;GuiZhou University,GuiYan, 550025, China;Northwest Minorities University, Lanzhou 730030, China
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

PVM (Parallel virtual machine) library is a tool which used processes large amounts of data sets. This paper wants to achieve a high performance solution that exploits PVM library and parallel computers to solve ICA (Independent Component Analysis) problem. The paper presents parallel power ICA implementations to decomposition data sets. Power iteration (PI) is an algorithm for independent component analysis, which has some desired features. It has higher performance and data capacity than current sequential implementations. This paper, we show the power iteration algorithm which learning updating is in the form of matrix transformation . From power iteration algorithm, we develop parallel power iteration algorithm and implement parallel component decomposition solution. At last, experimental results, analysis and future plans are presented.