A New Approach to Multi-class Linear Dimensionality Reduction

Linear dimensionality reduction (LDR) is quite important in pattern recognition due to its efficiency and low computational complexity. In this paper, we extend the two-class Chernoff-based LDR method to deal with multiple classes. We introduce the criterion, as well as the algorithm that maximizes such a criterion. The proof of convergence of the algorithm and a formal procedure to initialize the parameters of the algorithm are also given. We present empirical simulations on standard well-known multi-class datasets drawn from the UCI machine learning repository. The results show that the proposed LDR coupled with a quadratic classifier outperforms the traditional LDR schemes.

• Type: Conference
• Year: 2006
• Where: CIARP
• Authors: Luis Rueda, Myriam Herrera
• Keywords: CIARP 2006, Linear Dimensionality Reduction, Pattern Recognition, Traditional Ldr Schemes, Two-class Chernoff-based Ldr