Multidimensional Scaling for Genomic Data

Audrone Jakaitiene (), Mara Sangiovanni (), Mario R. Guarracino () and Panos M. Pardalos ()
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Audrone Jakaitiene: Vilnius University
Mara Sangiovanni: National Research Council
Mario R. Guarracino: National Research Council
Panos M. Pardalos: University of Florida

A chapter in Advances in Stochastic and Deterministic Global Optimization, 2016, pp 129-139 from Springer

Abstract: Abstract Scientists working with genomic data face challenges to analyze and understand an ever-increasing amount of data. Multidimensional scaling (MDS) refers to the representation of high dimensional data in a low dimensional space that preserves the similarities between data points. Metric MDS algorithms aim to embed inter-point distances as close as the input dissimilarities. The computational complexity of most metric MDS methods is over O(n 2), which restricts application to large genomic data (n ≫ 106). The application of non-metric MDS might be considered, in which inter-point distances are embedded considering only the relative order of the input dissimilarities. A non-metric MDS method has lower complexity compared to a metric MDS, although it does not preserve the true relationships. However, if the input dissimilarities are unreliable, too difficult to measure or simply unavailable, a non-metric MDS is the appropriate algorithm. In this paper, we give overview of both metric and non-metric MDS methods and their application to genomic data analyses.

Keywords: metric multidimensional scaling; non-metric multidimensional scaling; data mining; genomic data (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-29975-4_7

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