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We consider the problem of nonnegative tensor factorization. Our aim is to derive an efficient algorithm that is also suitable for parallel implementation. We adopt the alternating optimization (AO) framework and solve each matrix nonnegative least-squares problem via a Nesterov-type algorithm for strongly convex problems. We describe a parallel implementation of the algorithm and measure the speedup...
A parallel algorithm for low-rank tensor decomposition that is especially well-suited for big tensors is proposed. The new algorithm is based on parallel processing of a set of randomly compressed, reduced-size ‘replicas’ of the big tensor. Each replica is independently decomposed, and the results are joined via a master linear equation per tensor mode. The approach enables massive parallelism with...
In applications of tensor analysis, missing data is an important issue that is usually handled via weighted least-squares fitting, imputation, or iterative expectation-maximization. The resulting algorithms are often cumbersome, and tend to fail when the percentage of missing samples is large. This paper proposes a novel and refreshingly simple approach for handling randomly missing values in big...
Thousands of scientific conferences happen every year, and each involves a laborious scientific peer review process conducted by one or more busy scientists serving as Technical/Scientific Program Committee (TPC) chair(s). The chair(s) must match submitted papers to their reviewer pool in such a way that i) each paper is reviewed by experts in its subject matter, and ii) no reviewer is overloaded...
Power control has been extensively studied as an important way of mitigating interference and providing minimum signal to interference plus noise ratio (SINR) guarantees. Such formulation of power control is well-motivated in cellular PCS and UMTS, as both voice and streaming media require guaranteed short-term rates. A key difficulty is that the problem can easily become infeasible, implying that...
Power control is important in interference-limited cellular, ad-hoc, and cognitive wireless networks, when the objective is to ensure a certain quality of service to each connection. Power control has been extensively studied in this context, including distributed algorithms that are particularly appealing in ad-hoc and cognitive settings. A long-standing issue is that the power control problem may...
We present a frequency-domain technique based on PARAllel FACtor (PARAFAC) analysis that performs multichannel blind source separation (BSS) of convolutive speech mixtures. PARAFAC algorithms are combined with a dimensionality reduction step to significantly reduce computational complexity. The identifiability potential of PARAFAC is exploited to derive a BSS algorithm for the under-determined case...
We consider the problem of tracking the frequency and complex amplitude of a time-varying (TV) harmonic signal using particle filtering (PF) tools. Similar to previous PF approaches to TV spectral analysis, we assume that the frequency and complex amplitude evolve according to a Gaussian AR(1) model; but we concentrate on the important special case of a single TV harmonic. For this case, we show that...
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