Abstract
Adaptive feature extraction is useful in many information processing systems. This paper proposes a learning machine implemented via a neural network to perform such a task using the tool principal component analysis. This machine (1) is adaptive to nonstationary input, (2) is based on an unsupervised learning concept and requires no knowledge of if, or when, the input changes statistically, and (3) performs online computation that requires little memory or data storage. Associated with this machine, the authors propose a learning algorithm (LEAP), whose convergence properties are theoretically analyzed and whose performance is evaluated via computer simulations. Two major contributions of this paper are: (1) Under appropriate conditions, the authors prove that the algorithm will extract multiple principal components, when the learning rate is constant; and (2) they identify a near optimal domain of attraction.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Published Version
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