Dimensionality Reduction: From General Formulation to Data Representation

School of Computer Engineering

Nanyang Technological University

Singapore

Abstract:Over the past few decades, a large family of algorithms has been designed to provide different solutions to the problem of dimensionality reduction. In this talk, I will first describe a general formulation known as graph embedding to unify them within a common framework. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. I will then introduce a series of new dimensionality reduction algorithms developed under this framework, in which image objects are represented in their intrinsic forms and orders (e.g., an image is a second-order tensor (i.e., a matrix) and sequential data such as video sequences used in event analysis is in the form of a third-order tensor). Moreover, I will also present the applications of our dimensionality reduction algorithms for face recognition and human gait recognition.

Short Biography: Dr. Dong Xu is currently an associate professor in the School of Computer Engineering, Nanyang Technological University, Singapore. His research focuses on new theories, algorithms and systems for intelligent processing and understanding of visual data such as images and videos. One of his co-authored works on domain adaptation for video event recognition won the Best Student Paper Award in CVPR 2010. He is on the editorial boards of IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems, and Machine Vision and Applications (Springer).