Research Repository

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

Tan, Xue-Min and Chen, Min-You and Gan, John Q (2015) 'A co-training algorithm based on modified Fisher's linear discriminant analysis.' Intelligent Data Analysis, 19 (2). pp. 279-292. ISSN 1088-467X

Full text not available from this repository.


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.

Item Type: Article
Uncontrolled Keywords: Semi-supervised learning; co-training; Fisher's linear discriminant analysis (FLDA); common spatial patterns (CSP); brain-computer interface (BCI)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 25 Jun 2015 11:08
Last Modified: 15 Jan 2022 00:26

Actions (login required)

View Item View Item