Manifold Learning: Graph Laplacian and Dimensionality Reduction
Speaker:Prof. Li Luoqing
Hubei University
Date & Time:11 Nov 2008 (Tuesday) 16:00 - 17:00
Venue:J215

Abstract

In many real-world problems, we need to deal with high-dimensional data. Unfortunately, what is so called as "the curse of dimensionality" states that dealing with a problem with a high-dimensional input can be very difficult in the worst case unless there is some regularities in the problem which we exploit. There is a growing interest in Machine Learning, in applying geometrical and topological tools to high-dimensional data analysis and processing. In this talk, we introduce some prominent manifold learning methods such as Isomap, Locally Linear Embedding, and Laplacian Eigenmaps. Manifold learning is formulated in terms of finding an embedding of a manifold into a lower dimensional space. These are basically nonlinear dimension reduction methods.