A simple and efficient Bayesian procedure for selecting dimensionality in multidimensional scaling

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Multidimensional scaling (MDS) is a technique which retrieves the locations of objects in a Euclidean space (the object configuration) from data consisting of the dissimilarities between pairs of objects. An important issue in MDS is finding an appropriate dimensionality underlying these dissimilarities. In this paper, we propose a simple and efficient Bayesian approach for selecting dimensionality in MDS. For each column (attribute) vector of an MDS configuration, we assume a prior that is a mixture of the point mass at 0 and a continuous distribution for the rest of the parameter space. Then the marginal posterior distribution of each column vector is also a mixture of the same form, in which the mixing weight of the continuous distribution is a measure of significance for the column vector. We propose an efficient Markov chain Monte Carlo (MCMC) method for estimating the mixture posterior distribution.The proposed method is fully Bayesian. It takes parameter estimation error into account when computing penalties for complex models and provides an uncertainty measure for the choice of dimensionality. Also, the MCMC algorithm is computationally very efficient since it visits various dimensional models in one MCMC procedure. A simulation study compares the proposed method with the Bayesian method of Oh and Raftery (2001). Three real data sets are analysed by using the proposed method.

Original languageEnglish
Pages (from-to)200-209
Number of pages10
JournalJournal of Multivariate Analysis
StatePublished - May 2012

Bibliographical note

Funding Information:
The author is grateful to the two reviewers and the associate editor for their valuable comments. This work was supported by the Korea Research Foundation (KRF) grant funded by the Korea government (MEST) 2009-0071093 .


  • Dissimilarity
  • Markov chain Monte Carlo
  • Mixture
  • Model selection


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