Structural damage identification has received considerable attention during the past
decades. Although several reviews have been presented, some new developments have …

Imaging spectroscopy involves the sensing of a large amount of spatial information across a
multitude of wavelengths. Conventional approaches to hyperspectral sensing scan adjacent …

A vast majority of machine learning algorithms train their models and perform inference by
solving optimization problems. In order to capture the learning and prediction problems …

As a convex relaxation of the rank minimization model, the nuclear norm minimization
(NNM) problem has been attracting significant research interest in recent years. The …

Feature selection is a significant task in data mining and machine learning applications
which eliminates irrelevant and redundant features and improves learning performance. In …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

This first chapter formulates the objectives of compressive sensing. It introduces the
standard compressive problem studied throughout the book and reveals its ubiquity in many …

Robust detection of infrared small and dim targets with highly heterogeneous backgrounds
plays an indispensable role in infrared search and tracking (IRST) system, which is still a …

Low rank matrix approximation (LRMA), which aims to recover the underlying low rank
matrix from its degraded observation, has a wide range of applications in computer vision …

The log-sum function as a penalty has always been drawing widespread attention in the
field of sparse problems. However, it brings a non-convex, non-smooth and non-Lipschitz …