Feature selection is one of the key problems for machine learning and data mining. In this
review paper, a brief historical background of the field is given, followed by a selection of …

This paper gives an overview of the majorization-minimization (MM) algorithmic framework,
which can provide guidance in deriving problem-driven algorithms with low computational …

Hyperspectral imagery contains hundreds of contiguous bands with a wealth of spectral
signatures, making it possible to distinguish materials through subtle spectral discrepancies …

Structured pruning is a commonly used technique in deploying deep neural networks
(DNNs) onto resource-constrained devices. However, the existing pruning methods are …

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 …

Sparse coding—that is, modelling data vectors as sparse linear combinations of basis
elements—is widely used in machine learning, neuroscience, signal processing, and …

Geographical characteristics derived from the historical check-in data have been reported
effective in improving location recommendation accuracy. However, previous studies mainly …

Recently, regression analysis has become a popular tool for face recognition. Most existing
regression methods use the one-dimensional, pixel-based error model, which characterizes …

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 …