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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Sensor localization technology is the principle problem for configuration and operation of wireless sensor network. Since existing multidimensional scaling localization algorithm has its limitation in localization accuracy, a novel method based on node distance correction (DCMDS) is put forward. The improved multidimensional scaling-based sensor localization algorithm partly divides the locatio...

Purpose – The purpose of this article is to investigate obesity-related queries from a public health portal (HealthLink) transaction log. Design/methodology/approach – Multidimensional scaling (MDS) was applied to each of five obesity-related focus keywords and their co-occurring terms in submitted queries. After the transaction log data were collected and cleaned, and query terms were extracte...

Previous algorithms for multidimensional scaling, or MDS, aim for scalable performance as the number of points to lay out increases. However, they either assume that the distance function is cheap to compute, and perform poorly when the distance function is costly, or they leave the precise number of distances to compute as a manual tuning parameter. We present Glint, an MDS algorithm framework...

Stock market indices SMIs are important measures of financial and economical performance. Considerable research efforts during the last years demonstrated that these signals have a chaotic nature and require sophisticated mathematical tools for analyzing their characteristics. Classical methods, such as the Fourier transform, reveal considerable limitations in discriminating different periods o...