In this paper, we propose a robust linear discriminant analysis (RLDA) through
Bhattacharyya error bound optimization. RLDA considers a nonconvex problem with the L 1 …

Linear discriminant analysis (LDA) is a popular classifier that is built on the assumption of
common population covariance matrix across classes. The performance of LDA depends …

Dimension reduction is a critical technology for high-dimensional data processing, where
Linear Discriminant Analysis (LDA) and its variants are effective supervised methods …

Sparse Discriminant Analysis (SDA) has been widely used to improve the performance of
classical Fisher's Linear Discriminant Analysis in supervised metric learning, feature …

Abstract Linear Discriminant Analysis (LDA) is a well-known technique for feature extraction
and dimension reduction. The performance of classical LDA however, significantly degrades …

We consider in this work small random perturbations of a nonlinear oscillator with friction–
type dissipation. We rigorously prove that under non–degenerate perturbations of …

In this paper, we explore advanced algorithmic frameworks for addressing the challenges in
non-convex optimization. We introduce inertial dynamical systems equipped with explicit …

Abstract Gaussian Graphical Model is widely used to understand the dependencies
between variables from high-dimensional data and can enable a wide range of applications …

We study the convergence behavior of a stochastic heavy-ball method with a small stepsize.
Under a change of time scale, we approximate the discrete scheme by a stochastic …