Two Neural Network Methods for Multidimensional Scaling

摘要

Multidimensional scaling (MDS) embeds points in a Euclidean space given only dissimilarity data. Only very recently MDS has gotten some attention from neural network researchers. We propose two neural network methods for MDS and evaluate them using both artificially generated and real data. Training uses two inputs at a time.