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1-Bit matrix completion

Mark A. Davenport, Yaniv Plan, Ewout van den Berg and Mary Wootters.

in Information and Inference: A Journal of the IMA

September 2014; p ublished online July 2014 .

Journal Article. Subjects: Science and Mathematics; Mathematics; Applied Mathematics; Computer Science. 0 words.

In this paper, we develop a theory of matrix completion for the extreme case of noisy 1-bit observations. Instead of observing a subset of the real-valued entries of a matrix null...

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CGIHT: conjugate gradient iterative hard thresholding for compressed sensing and matrix completion

Jeffrey D. Blanchard, Jared Tanner and Ke Wei.

in Information and Inference: A Journal of the IMA

December 2015; p ublished online November 2015 .

Journal Article. Subjects: Science and Mathematics; Mathematics; Applied Mathematics; Computer Science. 16576 words.

We introduce the conjugate gradient iterative hard thresholding (CGIHT) family of algorithms for the efficient solution of constrained underdetermined linear systems of equations arising in...

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Compressed subspace matching on the continuum

William Mantzel and Justin Romberg.

in Information and Inference: A Journal of the IMA

June 2015; p ublished online April 2015 .

Journal Article. Subjects: Science and Mathematics; Mathematics; Applied Mathematics; Computer Science. 12813 words.

We consider the general problem of matching a subspace to a signal in [math] that has been observed in-directly (compressed) through a random projection. We are interested in the case where...

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Deep Haar scattering networks

Xiuyuan Cheng, Xu Chen and Stéphane Mallat.

in Information and Inference: A Journal of the IMA

June 2016; p ublished online April 2016 .

Journal Article. Subjects: Science and Mathematics; Mathematics; Applied Mathematics; Computer Science. 12296 words.

An orthogonal Haar scattering transform is a deep network computed with a hierarchy of additions, subtractions and absolute values over pairs of coefficients. Unsupervised learning...

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Detecting the large entries of a sparse covariance matrix in sub-quadratic time

Ofer Shwartz and Boaz Nadler.

in Information and Inference: A Journal of the IMA

September 2016; p ublished online March 2016 .

Journal Article. Subjects: Science and Mathematics; Mathematics; Applied Mathematics; Computer Science. 10832 words.

The covariance matrix of a [math]-dimensional random variable is a fundamental quantity in data analysis. Given [math] i.i.d. observations, it is typically estimated by the sample...

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Deterministic Bayesian information fusion and the analysis of its performance

Gaurav Thakur.

in Information and Inference: A Journal of the IMA

December 2014; p ublished online December 2014 .

Journal Article. Subjects: Science and Mathematics; Mathematics; Applied Mathematics; Computer Science. 9764 words.

This paper develops a mathematical and computational framework for analyzing the expected performance of Bayesian data fusion, or joint statistical inference, within a sensor network. We...

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Disparity and optical flow partitioning using extended Potts priors

Xiaohao Cai, Jan Henrik Fitschen, Mila Nikolova, Gabriele Steidl and Martin Storath.

in Information and Inference: A Journal of the IMA

March 2015; p ublished online December 2014 .

Journal Article. Subjects: Science and Mathematics; Mathematics; Applied Mathematics; Computer Science. 8344 words.

This paper addresses the problems of disparity and optical flow partitioning based on the brightness invariance assumption. We investigate new variational approaches to these problems with...

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Finite sample posterior concentration in high-dimensional regression

Nate Strawn, Artin Armagan, Rayan Saab, Lawrence Carin and David Dunson.

in Information and Inference: A Journal of the IMA

June 2014; p ublished online June 2014 .

Journal Article. Subjects: Science and Mathematics; Mathematics; Applied Mathematics; Computer Science. 0 words.

We study the behavior of the posterior distribution in high-dimensional Bayesian Gaussian linear regression models having pn, where p is the number of predictors and n is the sample...

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Finite sample posterior concentration in high-dimensional regression

N. Strawn, A. Armagan, R. Saab, L. Carin and D. Dunson.

in Information and Inference: A Journal of the IMA

March 2015; p ublished online October 2014 .

Journal Article. Subjects: Science and Mathematics; Mathematics; Applied Mathematics; Computer Science. 54 words.

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Graph connection Laplacian and random matrices with random blocks

Noureddine El Karoui and Hau-tieng Wu.

in Information and Inference: A Journal of the IMA

March 2015; p ublished online March 2015 .

Journal Article. Subjects: Science and Mathematics; Mathematics; Applied Mathematics; Computer Science. 16604 words.

Graph connection Laplacian (GCL) is a modern data analysis technique that is starting to be applied for the analysis of high-dimensional and massive datasets. Motivated by this technique,...

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