Bayesian multidimensional scaling procedure with variable selection

L. Lin, D. K.H. Fong

Research output: Contribution to journalArticle

Abstract

Multidimensional scaling methods are frequently used by researchers and practitioners to project high dimensional data into a low dimensional space. However, it is a challenge to integrate side information which is available along with the dissimilarities to perform such dimension reduction analysis. A novel Bayesian integrative multidimensional scaling procedure, namely Bayesian multidimensional scaling with variable selection, is proposed to incorporate external information on the objects into the analysis through the use of a latent multivariate regression structure. The proposed Bayesian procedure allows the incorporation of covariate information into the dimension reduction analysis through the use of a variable selection strategy. An efficient computational algorithm to implement the procedure is also developed. A series of simulation experiments and a real data analysis are conducted, and the proposed model is shown to outperform several benchmark models based on some measures commonly used in the literature.

LanguageEnglish (US)
Pages1-13
Number of pages13
JournalComputational Statistics and Data Analysis
Volume129
DOIs
StatePublished - Jan 1 2019

Fingerprint

Variable Selection
Scaling
Dimension Reduction
Side Information
Multivariate Regression
Computational Algorithm
Dissimilarity
High-dimensional Data
Simulation Experiment
Covariates
Data analysis
Efficient Algorithms
Integrate
Model-based
Benchmark
Series
Experiments
Model

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

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Bayesian multidimensional scaling procedure with variable selection. / Lin, L.; Fong, D. K.H.

In: Computational Statistics and Data Analysis, Vol. 129, 01.01.2019, p. 1-13.

Research output: Contribution to journalArticle

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