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A minor component analysis algorithm
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DOI：10.1016/S0893-6080(96)00063-9.png)
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摘要
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The eigenvectors corresponding to the smallest eigenvalues of the autocorrelation matrix of the input signals are defined as the minor components, which play a very important role in many fields of adaptive signal processing such as spectral estimation, total least squares processing, eigen-based bearing estimation, digital beamforming, moving target indication, and clutter cancellation. This paper proposes a learning algorithm which extracts adaptively the minor component. We will use the Rayleigh quotient as an energy function and prove both analytically and by simulation results that the weight vector provided by the proposed algorithm is guaranteed to converge to the minor component of the input signals. (C) 1997 Elsevier Science Ltd. All Rights Reserved.
Keyword:
neural networks
unsupervised learning
adaptive signal processing
minor components
Hebbian learning rule
Oja's learning rule
eigenvectors
parameter estimation
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