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Conference Paper
Soft Geodesic Kernel K-Means
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- Authors
- Jae Hwan Kim, Kwang-Hyun Shim, Seung Jin Choi
- Issue Date
- 2007-04
- Citation
- International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2007, pp.II429-II432
- Language
- English
- Type
- Conference Paper
- Abstract
- 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. © 2007 IEEE.
- KSP Keywords
- Clustering performance, Data clustering, Data manifold, Data sets, Data space, Feature space, Kernel K-means, Manifold structure, Numerical experiments, Real-world data, document clustering