Several recent randomized linear algebra algorithms rely upon fast dimension reduction methods. A popular choice is the Subsampled Randomized Hadamard Transform (SRHT). In this article, we address the efficacy, in the Frobenius and spectral norms, of an SRHT-based low-rank matrix approximation technique introduced by Woolfe, Liberty, Rohklin, and Tygert. We establish a slightly better Frobenius...

This paper is devoted to the study of the traditional estimator of the fourth-order cumulants matrix of a high-dimensional multivariate Gaussian white noise. If M represents the dimension of the noise and N the number of available observations, it is first established that this M × M matrix converges towards 0 in the spectral norm sens provided M 2 logN N → 0. The behaviour of the estimated fou...

Let T be a self-adjoint operator acting in a separable Hilbert space H. We establish a correspondence between the reducing subspaces of T that come from a spectral projection and the convex, norm-closed bands in the set of finite Borel measures on R. IfH is not separable, we still obtain a reducing subspace corresponding to each convex norm-closed band. These observations lead to a unified trea...

We prove that multilinear (tensor) analogues of many efficiently computable problems in numerical linear algebra are NP-hard. Our list here includes: determining the feasibility of a system of bilinear equations, deciding whether a 3-tensor possesses a given eigenvalue, singular value, or spectral norm; approximating an eigenvalue, eigenvector, singular vector, or the spectral norm; and determi...

We consider elliptic operators A on a bounded domain, that are compact perturbations of a selfadjoint operator. We first recall some spectral properties of such operators: localization of the spectrum and resolvent estimates. We then derive a spectral inequality that measures the norm of finite sums of root vectors of A through an observation, with an exponential cost. Following the strategy of...

We consider the problem of estimating the covariance of a collection of vectors given extremely compressed measurements of each vector. We propose and study an estimator based on back-projections of these compressive samples. We show, via a distribution-free analysis, that by observing just a single compressive measurement of each vector one can consistently estimate the covariance matrix, in b...

Principal component analysis (PCA) requires the computation of a low-rank approximation to a matrix containing the data being analyzed. In many applications of PCA, the best possible accuracy of any rank-deficient approximation is at most a few digits (measured in the spectral norm, relative to the spectral norm of the matrix being approximated). In such circumstances, existing efficient algori...

The interrelationship between baroreflex sensitivity expressed in ms/mm Hg (BRS) or in Hz/mm Hg (BRSf), carotid wall thickness (IMT), and age was investigated in hypertensive and normotensive subjects with respect to the mean inter-beat interval (IBI) and blood pressure (BP). BP monitoring was performed in 25 treated hypertensives (Hy; 47.4+/-9.2 years of age) and 23 normotensives (Norm; 44.5+/...