Subspace-constrained partial-update methods for reduced-complexity mode estimation in high-dimensional data sets
OwnerBrian G. Ageeinventor
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Abstract
An adaptive processor is configured to provide 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 adaptive processor 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.
Claims28
A method of implementing partial updates in an adaptive processor that adjusts weights to optimize an eigenvalue-based objective function, the method comprising:
- selecting, from a set of combiner weights, a set of update weights and a set of held weights;
- performing updates to the set of held weights within a reduced-dimensionality subspace and performing unconstrained updates to the set of update weights to produce updated combiner weights;
- and
- employing the updated combiner weights to determine at least one solution to an eigenequation.
A non-transitory computer-readable medium having computer readable program code stored thereon, the computer readable program code being executable by at least one adaptive processor for:
- selecting, from a set of combiner weights, a set of update weights and a set of held weights;
- performing updates to the set of held weights within a reduced-dimensionality subspace and performing unconstrained updates to the set of update weights to produce updated combiner weights;
- and
- employing the updated combiner weights to determine a solution to an eigenequation.
Dependencies are inferred from the claim text.