Journal ArticleDOI
I-Scal: Multidimensional scaling of interval dissimilarities
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TLDR
A new algorithm called I-Scal, based on iterative majorization, that has the advantage that each iteration is guaranteed to improve the solution until no improvement is possible is developed.About:
This article is published in Computational Statistics & Data Analysis.The article was published on 2006-11-01. It has received 46 citations till now. The article focuses on the topics: Multidimensional scaling & Interval (mathematics).read more
Citations
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Journal ArticleDOI
Matrix Factorization with Interval-Valued Data
TL;DR: This paper proposes matrix decomposition techniques that consider the existence of interval-valued data and shows that naive ways to deal with such imperfect data may introduce errors in analysis and present factorization techniques that are especially effective when the amount of imprecise information is large.
Proceedings ArticleDOI
Sensor localization with algebraic confidence
TL;DR: The concept of algebraic confidence is introduced, defined as the measure of belief provided by an algebraic algorithm without a priori information of ranging statistics, and the cost function used in the Circular-based Interval SMACOF (CIS) algorithm is modified and described to solve the corresponding optimization problem by means of majorization techniques.
Book ChapterDOI
K -Nearest Neighbour Classification for Interval-Valued Data
TL;DR: This paper adopts an optimistic approach to replace the ill-known values, that requires to compute sets of possible and necessary neighbours of an instance, and provides an easy way to compute such sets, as well as the decision rule that follows from them.
Journal ArticleDOI
Big Data and Intelligence
TL;DR: The purpose of this presentation is to motivate a greater discussion of what is Big Data, how it is transforming the future of finance and what are the essential opportunities and concerns when using Big Data.
Book ChapterDOI
Symbolic Multidimensional Scaling Versus Noisy Variables and Outliers
TL;DR: Basic terms of symbolic data analysis and symbolic multidimensional scaling are presented and empirical part simulation experiment results with application of Interscal and I-Scal are compared based on artificial data generated by cluster.Gen procedure.
References
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Journal ArticleDOI
Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
TL;DR: The fundamental hypothesis is that dissimilarities and distances are monotonically related, and a quantitative, intuitively satisfying measure of goodness of fit is defined to this hypothesis.
Journal ArticleDOI
Nonmetric multidimensional scaling: A numerical method
TL;DR: The numerical methods required in the approach to multi-dimensional scaling are described and the rationale of this approach has appeared previously.
Journal ArticleDOI
Some distance properties of latent root and vector methods used in multivariate analysis
TL;DR: In this paper, the authors derived necessary and sufficient conditions for a solution to exist in real Euclidean space for a multivariate multivariate sample of size n as points P1, P2,..., PI in a Euclidian space and discussed the interpretation of the distance A(Pi, Pj) between the ith and jth members of the sample.
Journal ArticleDOI
Modern Multidimensional Scaling: Theory and Applications
TL;DR: The four Purposes of Multidimensional Scaling, Special Solutions, Degeneracies, and Local Minima, and Avoiding Trivial Solutions in Unfolding are explained.