Sparse regularization has been attracting much attention in industrial applications over the
past few decades. By exploiting the latent data structure in low-dimensional subspaces, a …

While most classical approaches to Granger causality detection assume linear dynamics,
many interactions in real-world applications, like neuroscience and genomics, are inherently …

This monograph is about a class of optimization algorithms called proximal algorithms. Much
like Newton's method is a standard tool for solving unconstrained smooth optimization …

Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

Low-rank recovery models have shown potential for salient object detection, where a matrix
is decomposed into a low-rank matrix representing image background and a sparse matrix …

Sparse estimation methods are aimed at using or obtaining parsimonious representations of
data or models. They were first dedicated to linear variable selection but numerous …

The employed dictionary plays an important role in sparse representation or sparse coding
based image reconstruction and classification, while learning dictionaries from the training …

A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable
Regularizers Page 1 Statistical Science 2012, Vol. 27, No. 4, 538–557 DOI: 10.1214/12-STS400 …

Submodular functions are relevant to machine learning for at least two reasons:(1) some
problems may be expressed directly as the optimization of submodular functions and (2) the …

In recent years, a large amount of multi-disciplinary research has been conducted on sparse
models and their applications. In statistics and machine learning, the sparsity principle is …