Although counseling psychologists conduct a great deal of research that attempts to reveal the structure of a given data set, rarely if ever do they utilize scaling procedures, preferring instead to rely on factor analytic strategies. In this article, we give a short introduction to the use of multidimensional scaling (MDS), with specific emphasis on applications in counseling and vocational ps...

Multidimensional scaling (MDS) is a generic name for a family of algorithms that construct a configuration of points in a target metric space from information about inter-point distances measured in some other metric space. Large-scale MDS problems often occur in data analysis, representation and visualization. Solving such problems efficiently is of key importance in many applications. In this...

We detail the investigation of the first application of several dissimilarity measures for large-scale solar image data analysis. Using a solar-domain-specific benchmark dataset that contains multiple types of phenomena, we analyzed combinations of image parameters with different dissimilarity measures in order to determine which combinations will allow us to differentiate among the multiple so...

GIS-MCE is the main method in land evaluation,but it is a linear method and neglects multidimensional complexity of factors used in land evaluation, which leads to information loss. Multidimensional scaling (MDS) originates from psychoanalysis, which is used to describe multidimensional data in higher dimensions by transforming data in higher dimensions into geometry structure in Lower dimensio...

As one of the most promising nonlinear dimensionality reduction techniques, Isometric Mapping (ISOMAP) performs well only when the data belong to a single well-sampled manifold, where geodesic distances can be well approximated by the corresponding shortest path distances in a suitable neighborhood graph. Unfortunately, the approximation gets less and less precise generally as the number of edg...

Multidimensional Scaling (MDS) is a classical technique for embedding data in low dimensions, still widespread use today. In this paper we study MDS modern setting - specifically, high dimensions and ambient measurement noise. We show that as the noise level increases, suffers sharp breakdown depends on dimension level, derive an explicit formula point case of white then introduce MDS+, simple ...

A common way for researchers to model or graphically portray spatial knowledge of a large environment is by applying multidimensional scaling (MDS) to a set of pairwise distance estimations. We introduce two MDS-like techniques that incorporate people's knowledge of directions instead of (or in addition to) their knowledge of distances. Maps of a familiar environment derived from these procedur...

Multidimensional-scaling (MDS) is a dimensionality reduction tool used for information analysis, data visualization and manifold learning. Most MDS procedures find embedding of data points in low dimensional Euclidean (flat) domains, such that distances between the points are as close as possible to given interpoints dissimilarities. We present an efficient solver for Classical Scaling, a speci...

In this study we show that humans form very similar perceptual spaces when they explore parametrically-defined shell-shaped objects visually or haptically. A physical object space was generated by varying three shape parameters. Sighted participants explored pictures of these objects while blindfolded participants haptically explored 3D printouts of the objects. Similarity ratings were performe...

One of the most important issues that have absorbed the public opinion and expert community during the recent years, is the qualitative and quantitative aspects of the housing. There are several challenges related to this topic that includes the contexts of the construction, manufacturing, planning to social aspects, cultural, physical and architectural design. The thing that has a significant ...