This tutorial text gives a unifying perspective on machine learning by covering both
probabilistic and deterministic approaches-which are based on optimization techniques …

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in
electrical engineering, applied mathematics, statistics and computer science. This book …

We consider efficient methods for the recovery of block-sparse signals-ie, sparse signals that
have nonzero entries occurring in clusters-from an underdetermined system of linear …

Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition
of sparse or compressible signals that can be well approximated by just K¿ N elements from …

Compressive sensing (CS) is a new approach to simultaneous sensing and compression of
sparse and compressible signals based on randomized dimensionality reduction. To …

Compressive sensing of signals drawn from a Gaussian mixture model (GMM) admits closed-
form minimum mean squared error reconstruction from incomplete linear measurements. An …

In this paper, we address the problem of gesture recognition using the theory of random
projection (RP) and by formulating the whole recognition problem as an 1-minimization …

Unions of subspaces provide a powerful generalization of single subspace models for
collections of high-dimensional data; however, learning multiple subspaces from data is …

This paper builds theoretical foundations for the recovery of a newly proposed class of
smooth graph signals, approximately bandlimited graph signals, under three sampling …

Compressive sensing is a new signal processing paradigm that aims to encode sparse
signals by using far lower sampling rates than those in the traditional Nyquist approach. It …