A minor subspace analysis algorithm.
This paper proposes a learning algorithm which extracts adaptively the minor subspace spanned by the eigenvectors corresponding to the smallest eigenvalues of the autocorrelation matrix of an input signal. We show both analytically and by simulation results that the weight vectors provided by the proposed algorithm are guaranteed to converge to the minor subspace of the input signal. For wider applications, we also present the complex valued version of the proposed minor subspace analysis algorithm.
Luo
F L
FL
Lehrstuhl fur Allgemeine und Theor. Elektrotech., Erlangen-Nurnberg Univ.
Unbehauen
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