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"Horseshoes and hidden variables, how to interpret output from Kernel PCA and MDS decompositions."
Abstract:
Classical multidimensional scaling (MDS) is a method for visualizing high-dimensional point clouds by mapping to low-dimensional Euclidean space. This mapping is defined in terms of eigenfunctions of a matrix of inter-point proximities. I will show cases where a complex phenomena can be broken down into simple one dimensional components using versions of MDS, one example is joint work with S. Goel and P. Diaconis on roll call votes from the 2005
house of representatives votes. Another is joint work with J. Chakerian and uses a distance between trees to see differences inhierarchical trees built from sequence data at varying mutation rates.
Bio:
Susan P. Holmes is a Professor of Statistics and Associate Director of the Mathematical and Computational Sciences Interdisciplinary Program. Dr. Holmes' main themes of research are: Computational Biology, Computer intensive methods in multivariate statistics, Phylogenetic analysis of DNA sequences, Multivariate statistics applied to micro-array techniques.
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