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

Tan, XM and Chen, MY and Gan, JQ (2015) 'A co-training algorithm based on modified Fisher's linear discriminant analysis.' Intelligent Data Analysis, 19 (2). 279 - 292. ISSN 1088-467X

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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
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 25 Jun 2015 11:08
Last Modified: 30 Mar 2021 23:15

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