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...

The limitations of using self-organizing maps (SaM) for either clustering/vector quantization (VQ) or multidimensional scaling (MDS) are being discussed by reviewing recent empirical findings and the relevant theory. SaM 's remaining ability of doing both VQ and MDS at the same time is challenged by a new combined technique of online K-means clustering plus Sammon mapping of the cluster centroi...

In this paper, we investigate relative localization techniques based on internode distance measurements for small wireless networks. High precision ranging is assumed, which is achieved by using technologies such as ultra-wide band (UWB) ranging. A number of approaches are formulated and compared for relative location estimation, which include the Linear Least Squares (LLS) approach, the Maximu...

Multidimensional scaling (MDS) which is a fundamental problem in data analysis, can be formulated as a nonlinear global optimization problem. In order to solve it, some local, heuristic and global optimization methods are implemented. In another hand, to perform our new heuristic approach, the MDS problem is studied by using a multistart Levenberg-Marquardt method and, also for some small examp...

In this paper we present 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 SMACOF and it is implemented in an R package of the same name which is presented in this article. We extend th...