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Showing 1 to 6 of 6 for “"Support estimation"”.

  1. Regularized weighted Chebyshev approximations for support estimation

    We introduce a new method for estimating the support size of an unknown distribution which provably matches the performance bounds of the stateof-the-art techniques in the area and outperforms them in practice. In particular, we present both theoretical and computer simulation results that …

    uiuc Repository record for Regularized weighted Chebyshev approximations for support estimation (opens in a new tab)

  2. One-bit Compressed Sensing in the Presence of Noise

    … CS) for signal reconstruction and parameter estimation. </p><p>We first consider the problem of joint sparse support estimation with one-bit measurements in a distributed setting. Each node observes sparse signals with the same but unknown support. The goal is to minimize the probability of …

    syracuse-diss Repository record for One-bit Compressed Sensing in the Presence of Noise (opens in a new tab)

  3. Tackling Key Challenges to Guide Clinical Decisions in Cardiovascular Diseases

    … hand, is hindered by the fact that the common support assumption is not \textit{a priori} guaranteed to be valid in non-randomized data. This thesis develops and applies approaches that address these challenges in order to obtain clinically useful insights. In the first part of the thesis, we …

    mit Repository record for Tackling Key Challenges to Guide Clinical Decisions in Cardiovascular Diseases (opens in a new tab)

  4. Global optimization methods for localization in compressive sensing

    … dependence on G in the proposed framework supports the high-resolution provided by the virtual array aperture while using a small number of MIMO radar elements. The second part of the dissertation focuses on the sparse recovery problem at the heart of compressive sensing. An algorithm, …

    njit Repository record for Global optimization methods for localization in compressive sensing (opens in a new tab)

  5. New Models And Algorithms For Distribution Testing: Beyond Standard Sampling

    … is the first known bound independent of the support size of the distribution for this problem. Next, we use our algorithm for tolerant uniformity testing to get an Õ(𝜀⁻⁴)-query algorithm for monotonicity testing in the conditional sampling model, improving on the Õ(𝜀⁻²²)-query algorithm of …

    mit Repository record for New Models And Algorithms For Distribution Testing: Beyond Standard Sampling (opens in a new tab)