New learning algorithms for principal and minor subspace extraction are proposed. They differ from each other only in the sign, i.e. the algorithm can extract principal component and if simply altered by the sign, it can also serve as minor component extractor. And what's more, the learned weigth matrix contains information of true principal or minor eigenvectors. Simulations show that the algorithms are effective.
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