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A globally convergent MC algorithm with an adaptive learning rate

journal contribution
posted on 2012-01-01, 00:00 authored by D Peng, Z Yi, Yong XiangYong Xiang, H Zhang
This brief deals with the problem of minor component analysis (MCA). Artificial neural networks can be exploited to achieve the task of MCA. Recent research works show that convergence of neural networks based MCA algorithms can be guaranteed if the learning rates are less than certain thresholds. However, the computation of these thresholds needs information about the eigenvalues of the autocorrelation matrix of data set, which is unavailable in online extraction of minor component from input data stream. In this correspondence, we introduce an adaptive learning rate into the OJAn MCA algorithm, such that its convergence condition does not depend on any unobtainable information, and can be easily satisfied in practical applications.

History

Journal

IEEE transactions on neural networks and learning systems

Volume

23

Issue

2

Pagination

359 - 365

Publisher

IEEE

Location

Piscataway, N. J.

ISSN

2162-237X

eISSN

2162-2388

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2012, IEEE