Learning conditional independence structure for high-dimensional uncorrelated vector processes

Nguyen Tran Quang; Alexander Jung

ICASSP 2017accepted0 citations

Learning conditional independence structure for high-dimensional uncorrelated vector processes

Abstract

We formulate and analyze a graphical model selection method for inferring the conditional independence graph of a high-dimensional nonstationary Gaussian random process (time series) from a finite-length observation. The observed process samples are assumed uncorrelated over time but having a time-varying marginal distribution. The selection method is based on testing conditional variances obtained for small subsets of process components. This allows to cope with the high-dimensional regime, where the sample size can be (much) smaller than the process dimension. We characterize the required sample size such that the proposed selection method is successful with high probability.

BibTeX

@inproceedings{icassp2017_learningconditio,
  title = {Learning conditional independence structure for high-dimensional uncorrelated vector processes},
  author = {Nguyen Tran Quang and Alexander Jung},
  booktitle = {ICASSP 2017},
  year = {2017}
}