Distance covariance analysis

Benjamin R. Cowley, João D. Semedo, Amin Zandvakili, Matthew A. Smith, Adam Kohn, Byron M. Yu

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

We propose a dimensionality reduction method to identify linear projections that capture interactions between two or more sets of variables. The method, distance covariance analysis (DCA), can detect both linear and nonlinear relationships, and can take dependent variables into account. On previous testbeds and a new testbed that systematically assesses the ability to detect both linear and nonlinear interactions, DCA performs better than or comparable to existing methods, while being one of the fastest methods. To showcase the versatility of DCA, we also applied it to three different neurophysiological datasets.

Original languageEnglish (US)
StatePublished - Jan 1 2017
Event20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017 - Fort Lauderdale, United States
Duration: Apr 20 2017Apr 22 2017

Conference

Conference20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017
CountryUnited States
CityFort Lauderdale
Period4/20/174/22/17

ASJC Scopus subject areas

  • Artificial Intelligence
  • Statistics and Probability

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  • Cite this

    Cowley, B. R., Semedo, J. D., Zandvakili, A., Smith, M. A., Kohn, A., & Yu, B. M. (2017). Distance covariance analysis. Paper presented at 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, Fort Lauderdale, United States.