From Dirichlet Process mixture models to spectral clustering

Stefano Tonellato ()
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Stefano Tonellato: Department of Economics, University Of Venice CÃ Foscari

No 2017:33, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: This paper proposes a clustering method based on the sequential estimation of the random partition induced by the Dirichlet process. Our approach relies on the Sequential Importance Resampling (SIR) algorithm and on the estimation of the posterior probabilities that each pair of observations are generated by the same mixture component. Such estimates do not require the identification of mixture components, and therefore are not affected by label switching. Then, a similarity matrix can be easily built, allowing for the construction of a weighted undirected graph, where nodes represent individuals and edge weights quantify the similarity between pairs of individuals. The paper shows how, in such a context, spectral clustering techniques can be applied in order to identify homogeneous groups.

Keywords: Dirichlet process priors; sampling importance resampling; weighted graph; laplacian; spectral clustering (search for similar items in EconPapers)
JEL-codes: C11 C38 (search for similar items in EconPapers)
Pages: 18 pages
Date: 2017
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