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A fast learning algorithm for principal component extraction with data dependent learning rate

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3 Author(s)
Lijun Liu ; Sch. of Sci., Dalian Nat. Univ., Dalian, China ; Rendong Ge ; Jun Tie

We propose a fast adaptive learning algorithm for computing principal eigenvector of covariance matrix arisen in the field of signal processing, where the learning process has to be repeated in online manner. Compared with most existing neural algorithms, the proposed approach effectively makes use of the online estimation of eigenvalue to update the principal eigenvector, which makes the method works with an adaptive data dependent learning rate and thus demonstrates a fast convergence speed. Numerical experiment further shows that this data dependent learning rate in the proposed algorithm offers significant advantages over that of constant learning algorithm.

Published in:

Advanced Computational Intelligence (IWACI), 2010 Third International Workshop on

Date of Conference:

25-27 Aug. 2010