Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
559931 | Digital Signal Processing | 2011 | 12 Pages |
Abstract
In this paper, we propose new adaptive algorithms for the extraction and tracking of the least (minor) or eventually, principal eigenvectors of a positive Hermitian covariance matrix. The main advantage of our proposed algorithms is their low computational complexity and numerical stability even in the minor component analysis case. The proposed algorithms are considered fast in the sense that their computational cost is O(np) flops per iteration where n is the size of the observation vector and p
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