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2008

Identification of MCMC Samples for Clustering

11 years 6 months ago
Identification of MCMC Samples for Clustering
Abstract. For clustering problems, many studies use just MAP assignments to show clustering results instead of using whole samples from a MCMC sampler. This is because it is not straightforward to recognize clusters based on whole samples. Thus, we proposed an identification algorithm which constructs groups of relevant clusters. The identification exploits spectral clustering to group clusters. Although a naive spectral clustering algorithm is intractable due to memory space and computational time, we developed a memory-and-time efficient spectral clustering for samples of a MCMC sampler. In experiments, we show our algorithm is tractable for real data while the naive algorithm is intractable. For search query log data, we also show representative vocabularies of clusters, which cannot be chosen by just MAP assignments.
Kenichi Kurihara, Tsuyoshi Murata, Taisuke Sato
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where LKR
Authors Kenichi Kurihara, Tsuyoshi Murata, Taisuke Sato
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