Weighted kernel Fisher discriminant analysis for integrating heterogeneous data
Computational Statistics & Data Analysis
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Kernel Fisher Discriminant Analysis (KFDA) has achieved great success in pattern recognition recently. However, the training process of KFDA is too time consuming (even intractable) for a large training set, because, for a training set with n examples, both its between-class and within-class scatter matrices are of n脳nand the time complexity of the KFDA training process is ofO(n^3). Aiming at this problem, this paper employs Bagging technique to decrease the time-space cost of KFDA training process. In addition, this paper is more than just a simple application of Bagging. We have made an important adaptation which can further guarantee the performance of KFDA. Our experimental results demonstrate that the proposed method can not only greatly reduce the cost of time of the training process, but also achieve higher recognition accuracy than traditional KFDA and the simple application of Bagging.