Choice of Dimension Using Reversible Jump Markov Chain Monte Carlo in the Multidimensional Scaling

Author

Xiangyun, Qing ; Xingyu, Wang

Author_Institution

East China Univ. of Sci. & Technol., Shanghai

fYear

2007

Firstpage

597

Lastpage

601

Abstract

Multidimensional scaling is a powerful tool for dimensionality reduction in the field of pattern recognition and data mining. Based on the bayesian multidimensional scaling (MDS), we consider the problem of determining the number of intrinsic low dimensions of MDS as a model selection problem. A Reversible Jump Markov chain Monte Carlo (RJMCMC) algorithm is proposed for performing low-dimensional coordinate and choice of dimension simultaneously within the Bayesian framework. Experiments results on simulated data and real data are presented to demonstrate the effectiveness of our RJMCMC method.

Keywords

Bayes methods; Markov processes; Monte Carlo methods; data mining; pattern recognition; Bayesian multidimensional scaling; data mining; dimensionality reduction; model selection problem; pattern recognition; reversible jump Markov chain Monte Carlo; Bayesian methods; Data mining; Educational institutions; Electronic mail; Information science; Monte Carlo methods; Multidimensional systems; Pattern recognition; Statistics; Intrinsic Dimension; Multidimensional Scaling; Multivariate Bayesian Statistics; Reversible Jump Markov Chain Monte Carlo;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference, 2007. CCC 2007. Chinese

Conference_Location

Hunan

Print_ISBN

978-7-81124-055-9

Electronic_ISBN

978-7-900719-22-5

Type

conf

DOI

10.1109/CHICC.2006.4347477

Filename

4347477