Journal
ANNALS OF APPLIED STATISTICS
Volume 5, Issue 3, Pages 2131-2149Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/11-AOAS460
Keywords
Principal Components Analysis; biplot; dimension reduction; multidimensional scaling; visualization
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In order to find previously unknown subgroups in biomedical data and generate testable hypotheses, visually guided exploratory analysis can be of tremendous importance. In this paper we propose a new dissimilarity measure that can be used within the Multidimensional Scaling framework to obtain a joint low-dimensional representation of both the samples and variables of a multivariate data set, thereby providing an alternative to conventional biplots. In comparison with biplots, the representations obtained by our approach are particularly useful for exploratory analysis of data sets where there are small groups of variables sharing unusually high or low values for a small group of samples.
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