A Generalized Principal Component Extraction Algorithm and Its Convergence Analysis

GAO Yingbin; KONG Xiangyu; HU Changhua; ZHANG Huihui; HOU Li’an

doi:10.11999/JEIT151433

Volume 38 Issue 10

Oct. 2016

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GAO Yingbin, KONG Xiangyu, HU Changhua, ZHANG Huihui, HOU Li’an. A Generalized Principal Component Extraction Algorithm and Its Convergence Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2531-2537. doi: 10.11999/JEIT151433

Citation:

GAO Yingbin, KONG Xiangyu, HU Changhua, ZHANG Huihui, HOU Li’an. A Generalized Principal Component Extraction Algorithm and Its Convergence Analysis[J]. Journal of Electronics & Information Technology, 2016, 38(10): 2531-2537. doi: 10.11999/JEIT151433

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A Generalized Principal Component Extraction Algorithm and Its Convergence Analysis

doi: 10.11999/JEIT151433 cstr: 32379.14.JEIT151433

Funds:

The National Natural Science Foundation of China (61074072, 61374120), The National Science Fund for Distinguished Youth Scholars (61025014)

Received Date: 2015-12-17
Rev Recd Date: 2016-05-10
Publish Date: 2016-10-19

Abstract

Abstract

The generalized principal component analysis plays an important roles in many fields of modern signal processing. However, up to now, there are few algorithms, which can extract the generalized principal component adaptively. In this paper, a generalized principal component extraction algorithm, which has fast convergence speed, is proposed. The corresponding Deterministic Discrete Time (DDT) system of the proposed algorithm is analyzed and some conditions about the learning rate and initial weight vector are also obtained. Finally, computer simulation and practical application results show that compared with some existing algorithms, the proposed algorithm has faster convergence speed and higher estimation accuracy.
- Generalized principal component,
- Deterministic Discrete Time (DDT),
- Convergence analysis,
- Neural networks

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References(15)

References

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