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Orthogonal Oja algorithm
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DOI：10.1109/97.841157.png)
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Abstract
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In this letter, we propose an orthogonalized version of the Oja algorithm (OOja) that can be used for the estimation of minor and principal subspaces of a vector sequence. The new algorithm offers, as compared to Oja, such advantages as orthogonality of the weight matrix, which is ensured at each iteration, numerical stability, and a quite similar computational complexity.
Keywords:
householder transform
numerical stability
Oja algorithm
subspace estimation
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