A novel incremental linear discriminant analysis for multitask pattern recognition problems

Abstract

In this paper, we propose a new incremental linear discriminant analysis (ILDA) for multitask pattern recognition (MTPR) problems in which training samples of a specific recognition task are given one after another for a certain period of time and the task is switched to another related task in turn. The Pang et al.'s ILDA is extended such that a discriminant space of the current task is augmented with effective discriminant vectors that are selected from other related tasks based on the class separability. We call the selection and augmentation of such discriminant vectors knowledge transfer of feature subspaces. In the experiments, the proposed ILDA is evaluated for the four MTPR problems, each of which consists of three multi-class recognition tasks. The results demonstrate that the proposed ILDA outperforms the ILDA without the knowledge transfer with regard to both the class separability and recognition accuracy. From the experimental results, we confirm that the proposed knowledge transfer mechanism works well to construct effective discriminant feature spaces incrementally. © 2009 Springer Berlin Heidelberg.