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作者:

Liu, Lijun (Liu, Lijun.) | Xing, Hongjie (Xing, Hongjie.) | Nan, Dong (Nan, Dong.)

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摘要:

In this paper a simple infinity norm based neural network algorithm for estimation of the principal component is developed. It seems to be especially useful in applications with changing environment, where the learning process has to be repeated in on-line manner. Theoretical analysis shows the weight vector converges to the principal eigenvector asymptotically. In comparison with the existing algorithms, numerical simulation shows that the proposed algorithm demonstrates fast convergence and robustness for a slightly noisy Gaussian samples with some points having large magnitude and angle with respect to the principal direction. © 2008 IEEE.

关键词:

Eigenvalues and eigenfunctions Intelligent systems Neural networks Principal component analysis

作者机构:

  • [ 1 ] [Liu, Lijun]Department of Mathematics, Dalian Nationalities University, Dalian, 116600, China
  • [ 2 ] [Xing, Hongjie]College of Mathematics and Computer Science, Hebei University, Baoding, 071002, China
  • [ 3 ] [Nan, Dong]College of Applied Science, Beijing University of Technology, Beijing, 100022, China

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年份: 2008

页码: 1155-1159

语种: 英文

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WoS核心集被引频次: 0

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ESI高被引论文在榜: 0 展开所有

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