Visualization of the kernel hierarchical clustering using discriminant analysis

Abstract

The kernel functions are useful into the field of classification and give the value of the inner product of two vectors in a high dimensional feature space by a mapping which one-to-one corresponds to the kernel function.Kernel hierarchical clustering is a technique of clustering by mapping the data of pattern space to feature space using a kernel function. Kernel hierarchical clustering can obtain a good result, but it is unknown how the data of pattern space is mapped in the feature space. This paper will propose how to visualize the distribution of the data in the feature space by using the mapping which maps the data in the feature space into the lower dimensional space with supporting the structure of clusters in the feature space. Moreover, the paper will discuss the relation with the visualization results and the clustering results of kernel hierarchical clustering.