A co-training algorithm based on modified Fisher's linear discriminant analysis

Abstract

In this paper, a new co-training algorithm based on modified Fisher's Linear Discriminant Analysis (FLDA) is proposed for semi-supervised learning, which only needs a small set of labeled samples to train classifiers and is thus very useful in applications like brain-computer interface (BCI) design. Two classifiers, one aiming to maximize the normalized between-class diversity and the other to minimize the normalized within-class diversity, are proposed for the co-training process. A method with a confidence criterion is also proposed for selecting unlabeled data to expand training data set. The co-training algorithm is compared with a static FLDA method and a FLDA based on self-training algorithm on the data set 2a for BCI Competition IV, with statistical significance test. Experimental results show that the new co-training algorithm outperformed the other two methods and its average classification accuracy was improved iteration by iteration, demonstrating the convergence of the co-training process.