Adaptive algorithms for first principal eigenvector computation
Neural Networks
Theoretical Computer Science
Global Convergence of a PCA Learning Algorithm with a Constant Learning Rate
Computers & Mathematics with Applications
A robust and globally convergent PCA learning algorithm
Control and Intelligent Systems
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The one-unit Oja algorithm plays a very important role in the study of principal component analysis neural networks. In this paper, we propose an energy function whose steepest descent direction (i.e., negative gradient direction) is the same as the average evolution direction of the one-unit Oja algorithm, and the energy function has two global minimal points corresponding to the two converged points of the one-unit Oja algorithm and it has no other local minimal points