Many problems of recent interest in statistics and machine learning can be posed in the
framework of convex optimization. Due to the explosion in size and complexity of modern …

We present a survey on maritime object detection and tracking approaches, which are
essential for the development of a navigational system for autonomous ships. The electro …

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 …

Object detection is a fundamental step for automated video analysis in many vision
applications. Object detection in a video is usually performed by object detectors or …

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 …

We consider the empirical risk minimization problem for linear supervised learning, with
regularization by structured sparsity-inducing norms. These are defined as sums of …

Low rank and sparse representation based methods, which make few specific assumptions
about the background, have recently attracted wide attention in background modeling. With …

Sparse estimation methods are aimed at using or obtaining parsimonious representations of
data or models. While naturally cast as a combinatorial optimization problem, variable or …

The paper revisits the Bayesian group lasso and uses spike and slab priors for group
variable selection. In the process, the connection of our model with penalized regression is …