A Survey of CUDA-based Multidimensional Scaling on GPU Architecture

The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction which is defined as a process of mapping data from high-dimensional space into low-dimensional. One of the most popular methods for handling this problem is multidimensional scaling. Due to the technological advances, the dimensionality of the input data as well as the amount of processed data is increasing steadily but the requirement of processing these data within a reasonable time frame still remains an open problem. Recent development in graphics hardware allows to perform generic parallel computations on powerful hardware and provides an opportunity to solve many time-constrained problems in both graphical and non-graphical domain. The purpose of this survey is to describe and analyze recent implementations of multidimensional scaling algorithms on graphics processing units and present the applicability of these algorithms on such architectures based on the experimental results which show a decrease of execution time for multi-level approaches

The Analysis of Consumers\u27 Preferences with the Application of Multivariate Models : Hedonic Regression and Multidimensional Scaling

On durable goods markets declared behaviours of buyers rarley leads to the actual purchasing decisions. This fact poses a particular challenge for the analysis of the future reactions of consumers to the elements of the marketing mix. This study attempts to combine the results obtained from multidimensional scaling and hedonic modelling to assess both stated and revealed preferences with respect to the attributes attributes of a specific durable good, namely a smartphone. The assessment of consumers’ declared behaviours was obtained by analysing data from an on-line survey study with the application of multidimensional scaling. Simultaneously, the estimated hedonic model provided the information on consumers’ revealed preferences. The combined use of both approaches allowed for broader insight into the issue of consumers’ behaviours, particularly in relation to the existing market offer

Sensitivity and robustness in MDS configurations for mixed-type data: a study of the economic crisis impact on socially vulnerable Spanish people

Multidimensional scaling (MDS) techniques are initially proposed to produce pictorial representations of distance, dissimilarity or proximity data. Sensitivity and robustness assessment of multivariate methods is essential if inferences are to be drawn from the analysis. To our knowledge, the literature related to MDS for mixed-type data, including variables measured at continuous level besides categorical ones, is quite scarce. The main motivation of this work was to analyze the stability and robustness of MDS configurations as an extension of a previous study on a real data set, coming from a panel-type analysis designed to assess the economic crisis impact on Spanish people who were in situations of high risk of being socially excluded. The main contributions of the paper on the treatment of MDS configurations for mixed-type data are: (i) to propose a joint metric based on distance matrices computed for continuous, multi-scale categorical and/or binary variables, (ii) to introduce a systematic analysis on the sensitivity of MDS configurations and (iii) to present a systematic search for robustness and identification of outliers through a new procedure based on geometric variability notions.Gower distance, MDS configurations, Mixed-type data, Outliers identification, Related metric scaling, Survey data

Multidimensional Scaling on Multiple Input Distance Matrices

Multidimensional Scaling (MDS) is a classic technique that seeks vectorial representations for data points, given the pairwise distances between them. However, in recent years, data are usually collected from diverse sources or have multiple heterogeneous representations. How to do multidimensional scaling on multiple input distance matrices is still unsolved to our best knowledge. In this paper, we first define this new task formally. Then, we propose a new algorithm called Multi-View Multidimensional Scaling (MVMDS) by considering each input distance matrix as one view. Our algorithm is able to learn the weights of views (i.e., distance matrices) automatically by exploring the consensus information and complementary nature of views. Experimental results on synthetic as well as real datasets demonstrate the effectiveness of MVMDS. We hope that our work encourages a wider consideration in many domains where MDS is needed

Human response to vibration in residential environments (NANR209), Technical report 6 : determination of exposure-response relationships

This technical report presents the development of exposure-response relationships for the human response to vibration in residential environments. The data used to formulate the relationships presented in this report are those which were collected for the Defra funded project “NANR209: Human response to vibration in residential environments”, the main aim of which was the development of exposure-response relationships. Vibration caused by railway traffic, construction work, and internal sources outside of the residents’ control were considered. Response data was collected via face to face interviews with residents in their own homes. The questionnaire was presented as a neighbourhood satisfaction survey and gathered information on, among other things, annoyance caused by vibration and noise exposure. Development and implementation of the questionnaire used for the collection of response data is discussed in Technical Report 2 and Technical Report 5. Vibration exposure was determined via measurement and prediction in such a way that, where possible, an estimation of internal vibration exposure was established for each residence in which a questionnaire was completed. The measurement procedures and methods employed to estimate vibration exposure are detailed in Technical Report 1 and Technical Report 3. Estimations of noise exposure were also derived for each residence using the methods detailed in Technical Report 4

Graphical representation of blood group data of human populations

A general survey of various measures of diversity within and distance between populations in gene frequencies of blood group systems is given. A method of ordering populations by an overall measure of diversity within and of clustering populations in terms of differences in pattern of diversity in different blood group systems is developed. Principal coordinate analysis and multidimensional scaling are used to represent populations with given distances between them graphically in an appropriate dimensional Euclidean space. Such graphical representations together with dendrograms are of great help in studying interrelationships between populations. The methods are illustrated using blood group data on some human populations