Multidimensional scaling (MDS) was applied to the numerical taxonomy of Candida species based on isoenzyme profiles. Multidimensional scaling uses proximity measures to generate a spatial configuration of points in multidimensional space where distances between points reflect similarity among types. The biochemical profiles of 35 types of Candida species based on 26 tests consisting of isoenzym...

The TriVis algorithm for visualization of multidimensional data proximities in two dimensions is presented. The algorithm preserves maximum number of exact distances, has simple interpretation, and unlike multidimensional scaling (MDS) does not require costly minimization. It may also provide an excellent starting point significantly reducing the number of required iterations in MDS.

Principal component analysis (PCA) and multidimensional scaling (MDS) are a set of mathematical techniques which uncover the underlying structure of data by examining the relationships between variables. Both MDS and PCA use proximity measures such as correlation coefficients or Euclidean distances to generate a spatial configuration (map) of points where distances between points reflect the re...

We show that neural networks, with a suitable error function for backpropagation, can be successfully used for metric multidimensional scaling (MDS) (i.e., dimensional reduction while trying to preserve the original distances between patterns) and are in fact able to outdo the standard algebraic approach to MDS, known as classical scaling.

Aalto University, P.O. Box 11000, FI-00076 Aalto www.aalto.fi Author Teuvo Kohonen Name of the publication New Developments of Nonlinear Projections for the Visualization of Structures in Nonvectorial Data Sets Publisher School of Science Unit Department of Information and Computer Science Series Aalto University publication series SCIENCE + TECHNOLOGY 8/2011 Field of research Computer science ...

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...