BAYESIAN MULTIDIlVIENSIONAL SCALING AND CHOICE OF DIMENSION

Multidimensional scaling is widely used to handle data which consist of dissimilarity measures between pairs of objects or people. We deal with two major problems in metric multidimensional scaling configuration of objects and determination of the dimension of object configuration within a Bayesian framework. A Markov chain Monte Carlo algorithm is proposed for object configuration, along with a simple Bayesian criterion for choosing their effective dimension, called MDSIC. Simulation results are presented, as well as examples on real data. Our method provides better results than classical multidimensional scaling for object configuration, and MDSIC seems to work well for dimension choice in the examples considered.