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...

In the paper, two combinations (consecutive and integrated) of vector quantization methods (self-organizing map and neural gas) and multidimensional scaling (MDS) have been investigated and compared. The vector quantization is used to reduce the number of dataset items. The dataset with a smaller number of items is analyzed by multidimensional scaling in order to reduce the number of features o...

Multidimensional scaling (MDS) techniques are statistical techqnies that convert information about distances between a number of objects into a spatial representation of these objects. These techniques were first discussed systematically in psychometrics [Torgerson, 1958; Coombs, 1964] as multidimensional extensions of univariate psychophysics and sensory scaling. Because they needed considerab...

The smacof package offers a comprehensive implementation of multidimensional scaling (MDS) techniques in R. Since its first publication (De Leeuw and Mair 2009b) the functionality has been enhanced, several additional methods, features utilities were added. Major updates include complete re-implementation unfolding allowing for monotone dissimilarity transformations, including row-conditional, ...

Multidimensional scaling (MDS) is a set of data analysis techniques used to explore the structure of (dis)similarity data. MDS represents a set of objects as points in a multidimensional space in such a way that the points corresponding to similar objects are located close together, while those corresponding to dissimilar objects are located far apart. The investigator then attempts to “make se...