Compressive nonparametric graphical model selection for time series


Alexander Jung, Reinhard Heckel, Helmut Bölcskei, and Franz Hlawatsch


Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 769-773, May 2014.

DOI: 10.1109/ICASSP.2014.6853700

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We propose a method for inferring the conditional independence graph (CIG) of a high-dimensional discrete-time Gaussian vector random process from finite-length observations. Our approach does not rely on a parametric model (such as, e.g., an autoregressive model) for the vector random process; rather, it only assumes certain spectral smoothness properties. The proposed inference scheme is compressive in that it works for sample sizes that are (much) smaller than the number of scalar process components. We provide analytical conditions for our method to correctly identify the CIG with high probability.


Sparsity, graphical model selection, multitask learning, nonparametric time series, LASSO

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