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Multidimensional Scaling
, 2000
"... Multidimensional Scaling (MDS) describes a family of techniques for the analysis of proximity data on a set of stimuli to reveal the hidden structure underlying the data. The proximity data can come from similarity judgments, identification confusion matrices, grouping data, samedifferent errors or ..."
Abstract

Cited by 3 (0 self)
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Multidimensional Scaling (MDS) describes a family of techniques for the analysis of proximity data on a set of stimuli to reveal the hidden structure underlying the data. The proximity data can come from similarity judgments, identification confusion matrices, grouping data, samedifferent errors
Multidimensional Scaling
 Handbook of Statistics
, 2001
"... eflecting the importance or precision of dissimilarity # i j . 1. SOURCES OF DISTANCE DATA Dissimilarity information about a set of objects can arise in many different ways. We review some of the more important ones, organized by scientific discipline. 1.1. Geodesy. The most obvious application, ..."
Abstract

Cited by 41 (2 self)
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, perhaps, is in sciences in which distance is measured directly, although generally with error. This happens, for instance, in triangulation in geodesy. We have measurements which are approximately equal to distances, either Euclidean or spherical, depending on the scale of the experiment. In other
Acknowledgments Local multidimensional scaling
"... Local multidimensional scaling 2 Local multidimensional scaling In a visualization task, every nonlinear projection method needs to make a compromise between trustworthiness and continuity. In a trustworthy projection the visualized proximities hold in the original data as well, whereas a continuou ..."
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Cited by 29 (1 self)
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Local multidimensional scaling 2 Local multidimensional scaling In a visualization task, every nonlinear projection method needs to make a compromise between trustworthiness and continuity. In a trustworthy projection the visualized proximities hold in the original data as well, whereas a
Multidimensional Scaling Using Majorization
 SMACOF in R.” Journal of Statistical Software
, 2009
"... 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 ..."
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Cited by 17 (2 self)
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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
Inverse Multidimensional scaling
, 1995
"... For metric multidimensional scaling much attention is given to algorithms for computing the configuration for fixed dissimilarities. Here we study the inverse problem: what is the set of dissimilarity matrices that yield a given configuration as a stationary point? Characterisations of this set are ..."
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Cited by 1 (0 self)
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For metric multidimensional scaling much attention is given to algorithms for computing the configuration for fixed dissimilarities. Here we study the inverse problem: what is the set of dissimilarity matrices that yield a given configuration as a stationary point? Characterisations of this set
Multidimensional scaling and regression
 Statistica Applicata
, 1992
"... Constrained multidimensional scaling was put on a firm theoretical basis by Jan De Leeuw and Willem Heiser in the 1980's. There is a simple method of fitting, based on distance via innerproducts, and a numerically more complicated one that is truly based on leastsquares on distances. The uncon ..."
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Cited by 1 (0 self)
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Constrained multidimensional scaling was put on a firm theoretical basis by Jan De Leeuw and Willem Heiser in the 1980's. There is a simple method of fitting, based on distance via innerproducts, and a numerically more complicated one that is truly based on leastsquares on distances
Multidimensional Scaling by Deterministic Annealing
 Proceedings EMMCVPR'97, Lecture Notes In Computer Science
, 1997
"... . Multidimensional scaling addresses the problem how proximity data can be faithfully visualized as points in a lowdimensional Euclidian space. The quality of a data embedding is measured by a cost function called stress which compares proximity values with Euclidian distances of the respective poi ..."
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Cited by 30 (3 self)
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. Multidimensional scaling addresses the problem how proximity data can be faithfully visualized as points in a lowdimensional Euclidian space. The quality of a data embedding is measured by a cost function called stress which compares proximity values with Euclidian distances of the respective
Visualization Methodology for Multidimensional Scaling
 JOURNAL OF CLASSIFICATION
"... We discuss the application of interactive visualization techniques to multidimensional scaling (MDS). MDS in its conventional batch implementations is prone to uncertainties with regard to (a) local minima in the underlying optimization, (b) sensitivity to the choice of the optimization criterion, ..."
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Cited by 24 (2 self)
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We discuss the application of interactive visualization techniques to multidimensional scaling (MDS). MDS in its conventional batch implementations is prone to uncertainties with regard to (a) local minima in the underlying optimization, (b) sensitivity to the choice of the optimization criterion
Visualizations of Asymmetric Multidimensional Scaling
, 2014
"... Distances or dissimilarities among units are assumed to be symmetric in most cases of multidimensional scaling(MDS); consequently, it is not an easy task to deal with asymmetric distances. Current asymmetric MDS still face difficulties in the interpretation of results. This study proposes a simpler ..."
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Distances or dissimilarities among units are assumed to be symmetric in most cases of multidimensional scaling(MDS); consequently, it is not an easy task to deal with asymmetric distances. Current asymmetric MDS still face difficulties in the interpretation of results. This study proposes a simpler
Results 1  10
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79,872