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SSG Seminar Abstract


Estimating Dependency and Significance for High-Dimensional Data

Michael Siracusa
CSAIL, MIT


Understanding the dependency structure of a set of variables is a key component in various signal processing applications which involve data association. The simple task of detecting whether any dependency exists is particularly difficult when models of the data are unknown or difficult to characterize because of high-dimensional measurements. We review the use of nonparametric tests for characterizing dependency and how to carry out these tests with high-dimensional observations. In addition we present a method to assess the significance of the tests. Some simple experiments with audio-visual data will be presented.




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