抄録
In this paper we propose the kernel method to calculate a Mahalanobis distance in the feature space and propose a kernel fuzzy classifier with ellipsoidal regions in that space. In our proposed kernel method, we first select linearly independent vectors that form a basis of the subspace in the feature space and discard remaining vectors. By this method, the covariance matrix in the feature space is not singular, and we do not need to use singular value decomposition. Thus we achieve training speed-up compared with the conventional kernel method. We evaluate our method using blood cell data. The result shows that the generalization ability is better than that of the conventional fuzzy classifier, which is generated in the input space. In addition, we can confirm that our proposed kernel method is effective for training speed-up.
