Feature selection, as a data preprocessing strategy, has been proven to be effective and
efficient in preparing data (especially high-dimensional data) for various data-mining and …

Automatic target recognition (ATR) for military applications is one of the core processes
toward enhancing intelligence and autonomously operating military platforms. Spurred by …

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 …

In many large-scale video analysis scenarios, one is interested in localizing and recognizing
human activities that occur in short temporal intervals within long untrimmed videos. Current …

In this paper, we formulate object tracking in a particle filter framework as a structured multi-
task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT) …

Feature selection (FS) is an important component of many pattern recognition tasks. In these
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …

In this paper, we propose a new unsupervised spectral feature selection model by
embedding a graph regularizer into the framework of joint sparse regression for preserving …

We consider the problem of controlling a possibly unknown linear dynamical system with
adversarial perturbations, adversarially chosen convex loss functions, and partially …

Feature selection is important in both machine learning and pattern recognition.
Successfully selecting informative features can significantly increase learning accuracy and …

In image analysis, the images are often represented by multiple visual features (also known
as multiview features), that aim to better interpret them for achieving remarkable …