Affiliations: [a] State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing, China | [b] School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
Correspondence:
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Corresponding author: Xue-Min Tan, State Key Laboratory of Power Transmission Equipment & System Security and New Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China. E-mail: [email protected].
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.
Keywords: Semi-supervised learning, co-training, Fisher's linear discriminant analysis (FLDA), common spatial patterns (CSP), brain-computer interface (BCI)
