Pattern analysis often requires a pre-processing stage for extracting or selecting features in
order to help the classification, prediction, or clustering stage discriminate or represent the …

The curse of dimensionality poses severe challenges to both technical and conceptual
progress in neuroscience. In particular, it plagues our ability to acquire, process, and model …

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

This package includes both Mathematical Statistics: Basic Ideas and Selected Topics,
Volume I, Second Edition, as well as Mathematical Statistics: Basic Ideas and Selected …

This paper describes a discrete-time wavelet transform for which the Q-factor is easily
specified. Hence, the transform can be tuned according to the oscillatory behavior of the …

A number of techniques for the compressed sensing of imagery are surveyed. Various
imaging media are considered, including still images, motion video, as well as multiview …

A technique for array diagnosis using a small number of measured data acquired by a near-
field system is proposed. The technique, inspired by some recent results in the field of …

This paper presents a structured dictionary-based model for hyperspectral data that
incorporates both spectral and contextual characteristics of spectral samples. The idea is to …

Penalized least squares regression is often used for signal denoising and inverse problems,
and is commonly interpreted in a Bayesian framework as a Maximum a posteriori (MAP) …

We consider the problem of learning a low-dimensional signal model from a collection of
training samples. The mainstream approach would be to learn an overcomplete dictionary to …