The Riemannian geometry of covariance matrices has been essential to several successful
applications, in computer vision, biomedical signal and image processing, and radar data …

Data, which lie in the space P m, of m× m symmetric positive definite matrices,(sometimes
called tensor data), play a fundamental role in applications, including medical imaging …

Covariance matrices play important roles in many areas of mathematics, statistics, and
machine learning, as well as their applications. In computer vision and image processing …

Recently, the precise performance of the Generalized LASSO algorithm for recovering
structured signals from compressed noisy measurements, obtained via iid Gaussian …

Abstract Machine learning models often rely on sparsity, low-rank, orthogonality, correlation,
or graphical structure. The structure of interest in this chapter is geometric, specifically the …

Data encoded as symmetric positive definite (SPD) matrices frequently arise in many areas
of computer vision and machine learning. While these matrices form an open subset of the …

Numerous applications in computer vision and machine learning rely on representations of
data that are compact, discriminative, and robust while satisfying several desirable …

The Multivariate Gaussian Distribution Page 1 The Multivariate Gaussian Distribution Chuong
B. Do October 10, 2008 A vector-valued random variable X = [X1 ··· Xn] T is said to have a …

The present paper gives a short introduction to information geometry, by using a simple
model of an exponential family which is a dually flat Riemannian space. The paper then …

This work provides a computationally efficient and statistically consistent moment-based
estimator for mixtures of spherical Gaussians. Under the condition that component means …