The goal of this paper is to provide an example of how multidimensional scaling (MDS) can be used for stimuli development. The study described in this paper illustrates this process by developing affective mental imagery stimuli using the circumplex model of affect as a guide. The circumplex model of affect argues that all emotions can be described in terms of two underlying primary dimensions:...

In this paper we approach the problem of calibrating topologically a discrete camera mounted on a robotic pan-tilt basis. In particular this work is focused in choosing the coordinate system of the camera consistently with the basis. The topological calibrating methodology of the sensor is based on multidimensional scaling (MDS). The choice of the coordinate system is based in performing known ...

We assessed the qualitative accord between several automatic face recognition algorithms and human perceivers. By comparing modeland human-generated measures of the similarity between pairs of faces, we were able to evaluate the suitability of the algorithms as models of human face recognition. Multidimensional scaling (MDS) was used to create a spatial representation of the subject response pa...

Hybrid geostatistical models aim at mimicking depositional events. The resulting models have the capability to simulate realistic stratigraphic structures for a variety of environments. However, this family of algorithms requires a high degree of parameterization. Therefore, having a good knowledge of the model parameters sensitivity is vital for understanding the behavior of such models. In th...

Multidimensional scaling (MDS) is a technique for visualizing the relationships among data that are similar to each other on very many dimensions. For example, meanings of words such as indefinite pronouns are similar to each other by virtue of being expressed by the same indefinite pronoun in one language or another (Haspelmath 1997). If one compares indefinite pronouns of a large number of la...

This study investigates the correlations between perceptual and other process is involved, for example; the distance metric may be physical spaces of voiceless English fricatives /f 6 s 5 h/, using Multidimensional scaling (MDS) technique. The spatial representations were constructed from perceptual similarity judgments and various spectral distance measures [1]. The results show that physical ...

Abstract: Multidimensional scaling (MDS) represents objects as points in an Euclidean space so that the perceived distances between points can reflect similarity (or dissimilarity) between objects. To be practical, the dimension of the projected space usually is kept as low as possible. Thus, it is unavoidable that part of the information in the original proximity matrix will be lost in the MDS...

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...

Multidimensional scaling is a must-have tool for visual data miners, projecting multidimensional data onto a two-dimensional plane. However, what we see is not necessarily what we think about. In many cases, end-users do not take care of scaling the projection space with respect to the multidimensional space. Anyway, when using nonlinear mappings, scaling is not even possible. Yet, without scal...

Diffusion-limited aggregation is consistent with simple scaling. However, strong subdominant terms are present, and these can account for various earlier claims of anomalous scaling. We show this in detail for the case of multiscaling.