Time: 14h00, Wednesday, May 4, 2016
Location: Room 6, Building A14, Institute of Mathematics, 18 Hoang Quoc Viet, Cau Giay, Hanoi
Abstract:To classify a subject into one of several classes based on a set of features observed from the subject is a fundamental area in statistical inference and a wide range of applications, including economics,  information technology, and bio-informatics, to name but a few. Because of the advance in technologies, modern statistical studies often face classification problems with high-dimensional data, where the number of features p much larger than the sample size n. In this case, classical methods and results based on fixed p and large n are no longer applicable. In this talk, we will discuss the mathematical foundation of recent proposed linear classifiers such as sparse linear discriminant analysis, features annealed independence rule, linear programming discriminant, multi-steplinear discriminant analysis, … for high-dimensional data.