In this paper, we propose a user interface for online shopping that uses a two dimensional product map to present products. This map is created using multidimensional scaling (MDS). Dissimilarities between products are computed using an adapted version of Gower’s coefficient of similarity based on the attributes of the product. The user can zoom in and out by drawing rectangles. We show an appl...

Linear methods yield data points xn = r1φn,1 +r ∗ 2φn,2 + . . .+ rMφn,M = R φn, which are d-dimensional linear combinations of the original D-dimensional data points φn. . Principal Component Analysis (PCA, Jolliffe (2002)): Preserves the global covariance structure by decomposition of the covariance matrix Σφ,φ = RS R. . Metric Multidimensional Scaling (MDS, Cox & Cox (1994)): Preserves inner ...

Additionally, experiments comparing voice qualities were performed using vowels synthesized using averaged F0s, fluctuation-reduced F0s obtained using highor low-pass filters, and the original F0s with fine fluctuations, to determine which frequency bands of the F0 fluctuations were significant for perceiving voice quality of each group. The experiment results were analyzed using a multidimensi...

This paper presents the application of multidimensional scaling (MDS) analysis to data emerging from noninvasive lung function tests, namely the input respiratory impedance. The aim is to obtain a geometrical mapping of the diseases in a 3D space representation, allowing analysis of (dis)similarities between subjects within the same pathology groups, as well as between the various groups. The a...

Dimension reduction techniques are widely used for the analysis and visualization of complex sets of data. This paper compares two recently published methods for nonlinear projection: Isomap and Curvilinear Distance Analysis (CDA). Contrarily to the traditional linear PCA, these methods work like multidimensional scaling, by reproducing in the projection space the pairwise distances measured in...

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...

In this paper, we define a mobile self-localization (MSL) problem for sparse and/or mobile robotic sensor networks, and propose an algorithm, MA-MDS-MAP(P), based on MultiDimensional Scaling (MDS) for solving the problem. For sparse robotic sensor networks, all the existing localization algorithms fail to work properly due to the lack of distance or connectivity data to uniquely calculate the g...

Participants with personal and without personal experiences with the Earth as a sphere estimated large-scale distances between six cities located on different continents. Cognitive distances were submitted to a specific multidimensional scaling algorithm in the 3D Euclidean space with the constraint that all cities had to lie on the same sphere. A simulation was run that calculated respective 3...

The iterative spring model (Kopcsa and Schiebel, 1998) is a kind of multidimensional scaling algorithm (MDS) based on point mass mechanics, that embeds objects in a two dimensional Euclidean space and allows to visualize object relationships and cluster structure. This technique assumes that the similarity matrix for the data set under consideration is symmetric. However there are many interest...

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