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 artiicially generated and real data. Training uses two inputs at a time.

In this article, a heuristic version of Multidimensional Scaling (MDS) named , like MDS, maps objects into an Euclidean space, such that similarities are preserved. In addition of being more efficient than MDS it allows query-by-example type of query, which makes it suitable for a content-based retrieval purposes.

A multigrid approach for the efficient solution of large-scale multidimensional scaling (MDS) problems is presented. The main motivation is a recent application of MDS to isometry-invariant representation of surfaces, in particular, for expression-invariant recognition of human faces. Simulation results show that the proposed approach significantly outperforms conventional MDS algorithms.

Multidimensional Scaling (MDS) is a classic technique that seeks vectorial representations for data points, given the pairwise distances between them. However, in recent years, data are usually collected from diverse sources or have multiple heterogeneous representations. How to do multidimensional scaling on multiple input distance matrices is still unsolved to our best knowledge. In this pape...

Multidimensional scaling (MDS) is well known technique for analysis of multidimensional data. The most important part of implementation of MDS is minimization of STRESS function. The convergence rate of known local minimization algorithms of STRESS function is no better than superlinear. The regularization of the minimization problem is proposed which enables the minimization of STRESS by means...

An alternative perspective on dimensionality reduction is offered by Multidimensional scaling (MDS). MDS is another classical approach that maps the original high dimensional space to a lower dimensional space, but does so in an attempt to preserve pairwise distances. That is MDS addresses the problem of constructing a configuration of t points in Euclidean space by using information about the ...

The aim of Multidimensional Scaling (MDS) is to search for a geometrical pattern of n points, on the basis of experimental dissimilarities data between these points. For nonmetric MDS, one may use ordinal data as dissimilarities. In general, as these dissimilarities are empirical, they may be errorful. Thus, in order to obtain better scaling solutions, it is of great interest to reduce error in...

This paper presents results regarding the performance of multidimensional scaling (MDS) when used to create three-dimensional navigation maps. MDS aims at reducing high-dimensional space into low-dimensional landscapes. Combined with browsers which are capable of visualizing threedimensional object information by applying the conceptual basis of Virtual Reality Modeling Language (VRML), MDS ope...

The ability to browse vast amounts of scientific data is critical to facilitate science discovery. High performance Multidimensional Scaling (MDS) algorithm makes it a reality by reducing dimensions so that scientists can gain insight into data set from a 3D visualization space. As multidimensional scaling requires quadratics order of physical memory and computation, a major challenge is to des...

Multidimensional scaling (MDS) has been suggested as a useful tool for the evaluation of the quality of synthesized speech. However, it has not yet been extensively tested for its application in this specific area of evaluation. In a series of experiments based on data from the Blizzard Challenge 2008 the relations between Weighted Euclidean Distance Scaling and Simple Euclidean Distance Scalin...