Kernel discriminant analysis using composite vectors

In this paper, we propose a new kernel discriminant analysis using composite vectors (C-KDA). We show that employing composite vectors is similar to using more samples by analysis, which is a great advantage in classification problems when the size of training samples is small. Motivated by this, we apply composite vectors to kernel-based methods, which may have overfitting problems when training samples are not sufficient. Experimental results using several data sets from UCI machine learning repository show that C-KDA gives a better performance compared to other methods based on primitive input variables and linear discriminant analysis using composite vectors (C-LDA) when the training sample size is relatively small.