GP kernels for cross-spectrum analysis


Conference Paper

Multi-output Gaussian processes provide a convenient framework for multi-task problems. An illustrative and motivating example of a multi-task problem is multi-region electrophysiological time-series data, where experimentalists are interested in both power and phase coherence between channels. Recently, Wilson and Adams (2013) proposed the spectral mixture (SM) kernel to model the spectral density of a single task in a Gaussian process framework.In this paper, we develop a novel covariance kernel for multiple outputs, called the cross-spectral mixture (CSM) kernel. This new, flexible kernel represents both the power and phase relationship between multiple observation channels. We demonstrate the expressive capabilities of the CSM kernel through implementation of a Bayesian hidden Markov model, where the emission distribution is a multi-output Gaussian process with a CSM covariance kernel. Results are presented for measured multi-region electrophysiological data.

Duke Authors

Cited Authors

  • Ulrich, K; Carlson, DE; Dzirasa, K; Carin, L

Published Date

  • January 1, 2015

Published In

Volume / Issue

  • 2015-January /

Start / End Page

  • 1999 - 2007

International Standard Serial Number (ISSN)

  • 1049-5258

Citation Source

  • Scopus