| Paper: | SPTM-P12.3 | ||
| Session: | Estimation | ||
| Time: | Friday, May 21, 13:00 - 15:00 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, and Class. Thry & Apps. | ||
| Title: | ORTHOGONAL DECOMPOSITIONS OF MULTIVARIATE STATISTICAL DEPENDENCE MEASURES | ||
| Authors: | Ilan Goodman; Rice University | ||
| Don H. Johnson; Rice University | |||
| Abstract: | We describe two multivariate statistical dependence measures which can be orthogonally decomposed to separate the effects of pairwise, triplewise, and higher order interactions between the random variables. These decompositions provide a convenient method of analyzing statistical dependencies between large groups of random variables, within which smaller ''sub-groups'' may exhibit dependencies separately from the rest of the variables. The first dependence measure is a generalization of Pearson's phi-squared, and we decompose it using an orthonormal series expansion of joint probability density functions. The second measure is based on the Kullback-Leibler distance, and we decompose it using information geometry. Applications of these techniques include analysis of neural population recordings and multi-modal sensor fusion. We discuss in detail the simple example of three jointly defined binary random variables. | ||
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