In this study we show that humans are able to form a perceptual space from a complex, three-dimensional shape space that is highly congruent to the physical object space no matter if the participants explore the objects visually or haptically. The physical object space consists of complex, shell-shaped objects which were generated by varying three shape parameters. In several psychophysical exp...

Multidimensional scaling (MDS) is a data analysis technique for representing measurements of (dis)similarity among pairs of objects as distances between points in a low-dimensional space. MDS methods differ mainly according to the distance model used to scale the proximities. The most usual model is the Euclidean one, although a spherical model is often preferred to represent correlation measur...

This article is an updated version of De Leeuw and Mair (2009b) published in the Journal of Statistical Software. It elaborates on the methodology of multidimensional scaling problems (MDS) solved by means of the majorization algorithm. The objective function to be minimized is known as stress and functions which majorize stress are elaborated. This strategy to solve MDS problems is called SMAC...

This report discusses one paper for linear data dimensionality reduction, Eigenfaces, and two recently developed nonlinear techniques. The first nonlinear method, Locally Linear Embedding (LLE), maps the input data points to a single global coordinate system of lower dimension in a manner that preserves the relationships between neighboring points. The second method, Isomap, computes geodesic d...

Two experiments tested the hypothesis that implicit personality theory person types are composed of causally interconnected traits. Experiment 1 showed that the weakest trait member of a person type is perceived as more causally related to the core trait members of the type than are nonmember traits, even when those nonmember traits are both more highly correlated with and closer in multidimens...

Localization of sensors nodes is a key and fundamental issue in wireless sensor networks due to random deployment. In this paper, we propose a tree based clustering (TBC) multidimensional scaling algorithm for wireless sensor networks with the purpose of overcoming the shortage of classical MDS algorithms in its localization accuracy and computing complexity. Clustering is adopted to degrade th...

Abstract. Random Forest (RF) is a powerful ensemble method for classification and regression tasks. It consists of decision trees set. Although, a single tree is well interpretable for human, the ensemble of trees is a black-box model. The popular technique to look inside the RF model is to visualize a RF proximity matrix obtained on data samples with Multidimensional Scaling (MDS) method. Here...

Isomap [4] is a manifold learning algorithm, which extends classical multidimensional scaling (MDS) by considering approximate geodesic distance instead of Euclidean distance. The approximate geodesic distance matrix can be interpreted as a kernel matrix, which implies that Isomap can be solved by a kernel eigenvalue problem. However, the geodesic distance kernel matrix is not guaranteed to be ...

Recently, in response to industrial demand to develop faster and cheaper methods of descriptive analysis, several methods have been offered as alternatives to conventional profiling. We present three of these methods: sorting tasks, projective mapping, and flash profiling along with a new statistical method to analyze them. This new statistical method, called DISTATIS is a generalization of cla...

-Identifying the underlying decision criteria used by operators to classi&y system state and revealing the way in which that information is internally represented by individual operators is one of the major challenges facing designers of decision aids for process plants. This researqh describes the use of multidimensional scaling (MDS) to probe the structure and composition of the mental models...