The problem of recovering a structured signal x∈ C p from a set of dimensionality-reduced
linear measurements b= Ax arises in a variety of applications, such as medical imaging …

Linear encoding of sparse vectors is widely popular, but is commonly data-independent–
missing any possible extra (but a priori unknown) structure beyond sparsity. In this paper we …

We propose a novel framework for the deterministic construction of linear, near-isometric
embeddings of a finite set of data points. Given a set of training points X⊂\BBR N, we …

In this paper, we focus on two challenges which offset the promise of sparse signal
representation, sensing, and recovery. First, real-world signals can seldom be described as …

We introduce a learning-based algorithm to obtain a measurement matrix for compressive
sensing related recovery problems. The focus lies on matrices with a constant modulus …

Approaches to signal representation and coding theory have traditionally focused on how to
best represent signals using parsimonious representations that incur the lowest possible …

Linear encoding of sparse vectors is widely popular, but is most commonly dataindependent–
missing any possible extra (but a-priori unknown) structure beyond sparsity. In this paper we …

We propose algorithms for constructing linear embeddings of a finite dataset V⊂ ℝ d into a k-
dimensional subspace with provable, nearly optimal distortions. First, we propose an …

Visual retrieval and classification are of growing importance for a number of applications,
including surveillance, automotive, as well as web and mobile search. To facilitate these …

Choosing a distance preserving measure or metric is fundamental to many signal
processing algorithms, such as k-means, nearest neighbor searches, hashing, and …