Soft Geodesic Kernel K-Means
Jaehwan Kim, Kwang-Hyun Shim, Seungjin Choi · 2007
In this paper we present a kernel method for data clustering, where the soft k-means is carried out in a feature space, instead of input data space, leading to soft kernel k-means. We also incorporate a geodesic kernel into the soft kernel k-means, in order to take the data manifold structure into account. The method is referred to as soft geodesic kernel k-means. In contrast to k-means, our method is able to identify clusters that are not linearly separable. In addition, soft responsibilities as well as geodesic kernel, improve the clustering performance, compared to kernel k-means. Numerical experiments with toy data sets and real-world data sets (UCI and document clustering), confirm the useful behavior of the proposed method.