1,367 research outputs found
Blind Multilinear Identification
We discuss a technique that allows blind recovery of signals or blind
identification of mixtures in instances where such recovery or identification
were previously thought to be impossible: (i) closely located or highly
correlated sources in antenna array processing, (ii) highly correlated
spreading codes in CDMA radio communication, (iii) nearly dependent spectra in
fluorescent spectroscopy. This has important implications --- in the case of
antenna array processing, it allows for joint localization and extraction of
multiple sources from the measurement of a noisy mixture recorded on multiple
sensors in an entirely deterministic manner. In the case of CDMA, it allows the
possibility of having a number of users larger than the spreading gain. In the
case of fluorescent spectroscopy, it allows for detection of nearly identical
chemical constituents. The proposed technique involves the solution of a
bounded coherence low-rank multilinear approximation problem. We show that
bounded coherence allows us to establish existence and uniqueness of the
recovered solution. We will provide some statistical motivation for the
approximation problem and discuss greedy approximation bounds. To provide the
theoretical underpinnings for this technique, we develop a corresponding theory
of sparse separable decompositions of functions, including notions of rank and
nuclear norm that specialize to the usual ones for matrices and operators but
apply to also hypermatrices and tensors.Comment: 20 pages, to appear in IEEE Transactions on Information Theor
The condition number of join decompositions
The join set of a finite collection of smooth embedded submanifolds of a
mutual vector space is defined as their Minkowski sum. Join decompositions
generalize some ubiquitous decompositions in multilinear algebra, namely tensor
rank, Waring, partially symmetric rank and block term decompositions. This
paper examines the numerical sensitivity of join decompositions to
perturbations; specifically, we consider the condition number for general join
decompositions. It is characterized as a distance to a set of ill-posed points
in a supplementary product of Grassmannians. We prove that this condition
number can be computed efficiently as the smallest singular value of an
auxiliary matrix. For some special join sets, we characterized the behavior of
sequences in the join set converging to the latter's boundary points. Finally,
we specialize our discussion to the tensor rank and Waring decompositions and
provide several numerical experiments confirming the key results
Multidirectional Subspace Expansion for One-Parameter and Multiparameter Tikhonov Regularization
Tikhonov regularization is a popular method to approximate solutions of linear discrete ill-posed problems when the observed or measured data is contaminated by noise. Multiparameter Tikhonov regularization may improve the quality of the computed approximate solutions. We propose a new iterative method for large-scale multiparameter Tikhonov regularization with general regularization operators based on a multidirectional subspace expansion. The multidirectional subspace expansion may be combined with subspace truncation to avoid excessive growth of the search space. Furthermore, we introduce a simple and effective parameter selection strategy based on the discrepancy principle and related to perturbation results
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