Journal Article10.1109/97.841157
Orthogonal Oja algorithm
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TL;DR: An orthogonalized version of the Oja algorithm (OOja) is proposed that can be used for the estimation of minor and principal subspaces of a vector sequence and offers advantages as orthogonality of the weight matrix.
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Abstract: 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.
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TL;DR: A novel interpretation of the signal subspace as the solution of a projection like unconstrained minimization problem is presented, and it is shown that recursive least squares techniques can be applied to solve this problem by making an appropriate projection approximation.
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TL;DR: The Stochastic Gradient Ascent neural network is proposed and shown to be closely related to the Generalized Hebbian Algorithm (GHA), and the SGA behaves better for extracting the less dominant eigenvectors.
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The propagator method for source bearing estimation
TL;DR: It is found that at high and medium signal-to-noise ratio, the OPM performs quite like MUSIC with a complexity reduced by the ratio of the number of sources to theNumber of sensors.
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Global convergence of Oja's subspace algorithm for principal component extraction
TL;DR: The asymptotic convergence rates of the Oja algorithm are discovered and the dependence of the algorithm on its initial weight matrix and the singularity of the data covariance matrix is comprehensively addressed.
Spatio-temporal blind adaptive multiuser detection
TL;DR: It is seen that the blind adaptive multiuser detection and blind spatio-temporal signature estimation can be integrated jointly, and a blind estimation of the spatial signature based on the orthogonality between noise and signal subspaces is developed.
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