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| Titel: |
A co-training algorithm based on modified Fisher's linear discriminant analysis |
| Auteur: |
Tan, Xue-Min Chen, Min-You Gan, John Q. |
| Verschenen in: |
Intelligent data analysis |
| Paginering: | Jaargang 19 (2015) nr. 2 pagina's 279-292 |
| Jaar: |
2015-04-16 |
| Inhoud: |
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. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 3 van 26 gevonden artikelen
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