References of "Sidiropoulos, Nicholas D."
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See detailConvergence of the Huber Regression M-Estimate in the Presence of Dense Outliers
Tsakonas, Efthymios; Jaldén, Joakim; Sidiropoulos, Nicholas D. et al

in IEEE Signal Processing Letters (2014), 21(11), 1211-1214

We consider the problem of estimating a deterministic unknown vector which depends linearly on noisy measurements, additionally contaminated with (possibly unbounded) additive outliers. The measurement ... [more ▼]

We consider the problem of estimating a deterministic unknown vector which depends linearly on noisy measurements, additionally contaminated with (possibly unbounded) additive outliers. The measurement matrix of the model (i.e., the matrix involved in the linear transformation of the sought vector) is assumed known, and comprised of standard Gaussian i.i.d. entries. The outlier variables are assumed independent of the measurement matrix, deterministic or random with possibly unknown distribution. Under these assumptions we provide a simple proof that the minimizer of the Huber penalty function of the residuals converges to the true parameter vector with a root n-rate, even when outliers are dense, in the sense that there is a constant linear fraction of contaminated measurements which can be arbitrarily close to one. The constants influencing the rate of convergence are shown to explicitly depend on the outlier contamination level. [less ▲]

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See detailConvex Optimization-based Beamforming: From Receive to Transmit and Network Designs
Gershman, Alex B.; Sidiropoulos, Nicholas D.; Shahbazpanahi, Shahram et al

in IEEE Signal Processing Magazine (2010), 27(3), 62-75

In this article, an overview of advanced convex optimization approaches to multisensor beamforming is presented, and connections are drawn between different types of optimization-based beamformers that ... [more ▼]

In this article, an overview of advanced convex optimization approaches to multisensor beamforming is presented, and connections are drawn between different types of optimization-based beamformers that apply to a broad class of receive, transmit, and network beamformer design problems. It is demonstrated that convex optimization provides an indispensable set of tools for beamforming, enabling rigorous formulation and effective solution of both long-standing and emerging design problems. [less ▲]

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