We investigate whether the Pairwise Rating Method (PRaM) and Spatial Arrangement (SpAM) yield multidimensional scaling (MDS) solutions of comparable dimensionality. Across three studies that included twelve semantic categories with varying numbers both pictorial verbal exemplars, we did not find consistent dimensionality differences between two similarity measurement methods. The results allevi...

Multidimensional scaling (MDS) analysis is a dimension-reduction technique that is used to estimate the coordinates of a set of objects. However, not every criterion used in multidimensional scaling is equally and precisely weighted in the real world. To address this issue, we use fuzzy analytic hierarchy process (FAHP) to determine the weighting of subjective/perceptive judgments for each crit...

A common task in data mining is the visualization of multivariate objects using various methods, allowing human observers to perceive subtle inter-relations in the dataset. Multidimensional scaling (MDS) is a well known technique used for this purpose, but it due to its computational complexity there are limitations on the number of objects that can be displayed. Combining MDS with a clustering...

Waves of globalization reflect the historical technical progress and modern economic growth. The dynamics of this process are here approached using the multidimensional scaling (MDS) methodology to analyze the evolution of GDP per capita, international trade openness, life expectancy, and education tertiary enrollment in 14 countries. MDS provides the appropriate theoretical concepts and the ex...

Subjects rated the (dis)similarity of paired voice samples on a nine-point scale. The short voice samples were taken from the DyViS database of young male speakers with ‘Standard Southern British’ pronunciation. Accent was thus controlled, and ratings can be presumed to tap perceived personal voice quality differences. Multidimensional scaling (MDS) was applied to the ratings to derive five pse...

Overview From a non-technical point of view, the purpose of multidimensional scaling (MDS) is to provide a visual representation of the pattern of proximities (i.e., similarities or distances) among a set of objects. For example, given a matrix of perceived similarities between various brands of air fresheners, MDS plots the brands on a map such that those brands that are perceived to be very s...

Most of the geochemical datasets include missing data with different portions and this may cause a significant problem in geostatistical modeling or multivariate analysis of the data. Therefore, it is common to impute the missing data in most of geochemical studies. In this study, three approaches called half detection (HD), multiple imputation (MI), and the cosimulation based on Markov model 2...

Localization of sensor nodes is crucial in Wireless Sensor Network because of applications like surveillance, tracking, navigation etc. Various optimization techniques for localization have been proposed in literature by different researchers. In this paper, we propose a two phase hybrid approach for localization using Multidimensional Scaling and trilateration, namely, MDS with refinement usin...

We give a tutorial overview† of several geometric methods for feature selection and dimensional reduction. We divide the methods into projective methods and methods that model the manifold on which the data lies. For projective methods, we review projection pursuit, principal component analysis (PCA), kernel PCA, probabilistic PCA, and oriented PCA; and for the manifold methods, we review multi...

This paper reports recent progress towards the development of a spatial ear trainer. A study into the perceptual construct of ‘ensemble width’ was conducted. With the help of a novel surround panner, exemplary stimuli were created. Changes were highly controlled to enable unidimensional variation of the intended qualitative effect. To assess the success of the simulation, a subjective experimen...