Title :
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
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;
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
DOI :
10.1109/CHICC.2006.4347477
