Subspace-constrained partial update method for reduced-complexity mode estimation in high-dimensional data sets

Abstract

A method is described for reduced-complexity estimation of signal and data modes in high-dimensional data sets, by implementing subspace-constrained partial updates to optimize an eigenvalue-based objective function. The method selects, from a set of combiner weights, a set of update weights and a set of held weights; performs updates to the set of held weights within a reduced-dimensionality subspace and unconstrained updates to the set of update weights to produce updated combiner weights; and employs the updated combiner weights to determine at least one solution to an eigenequation or pseudo-eigenequation.

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