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Journal ArticleDOI

Invertibility of ‘large’ submatrices with applications to the geometry of Banach spaces and harmonic analysis

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TLDR
In this paper, the main problem of restricted invertibility of linear operators acting on finite dimensionallp-spaces is investigated, and the results obtained below enable us to complete earlier work on the structure of complemented subspaces of lp-space which have extremal euclidean distance.
Abstract
The main problem investigated in this paper is that of restricted invertibility of linear operators acting on finite dimensionallp-spaces. Our initial motivation to study such questions lies in their applications. The results obtained below enable us to complete earlier work on the structure of complemented subspaces ofLp-spaces which have extremal euclidean distance.

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Book

High-Dimensional Probability: An Introduction with Applications in Data Science

TL;DR: A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, machine learning, compressed sensing, and sparse regression.
Journal ArticleDOI

Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals

TL;DR: A new type of data acquisition system, called a random demodulator, that is constructed from robust, readily available components that supports the empirical observations, and a detailed theoretical analysis of the system's performance is provided.
Book

An Introduction to Matrix Concentration Inequalities

TL;DR: The matrix concentration inequalities as discussed by the authors are a family of matrix inequalities that can be found in many areas of theoretical, applied, and computational mathematics. But they are not suitable for the analysis of random matrices.

Compressive Sensing with structured random matrices

Holger Rauhut
TL;DR: These notes give a mathematical introduction to compressive sensing focusing on recovery using `1-minimization and structured random matrices and techniques for proving probabilistic estimates for condition numbers of structuredrandom matrices.
References
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Book ChapterDOI

On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities

TL;DR: This chapter reproduces the English translation by B. Seckler of the paper by Vapnik and Chervonenkis in which they gave proofs for the innovative results they had obtained in a draft form in July 1966 and announced in 1968 in their note in Soviet Mathematics Doklady.
Journal ArticleDOI

On the density of families of sets

TL;DR: This paper will answer the question in the affirmative by determining the exact upper bound of T if T is a family of subsets of some infinite set S then either there exists to each number n a set A ⊂ S with |A| = n such that |T ∩ A| = 2n or there exists some number N such that •A| c for each A⩾ N and some constant c.
Journal ArticleDOI

Probability Inequalities for the Sum of Independent Random Variables

TL;DR: In this article, a number of inequalities which improve on existing upper limits to the probability distribution of the sum of independent random variables are presented, which are applicable when the number of component random variables is small and/or have different distributions.
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