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A. Jung, R. Heckel, H. Bölcskei, F. Hlawatsch:
"Compressive nonparametric graphical model selection for time series";
Vortrag: IEEE International Conference on Speech, Acoustics, and Signal Processing 2014 (ICASSP 2014), Florence, Italy; 04.05.2014 - 09.05.2014; in: "ICASSP", IEEE, (2014), S. 769 - 773.

We propose a method for inferring the conditional indepen- dence graph (CIG) of a high-dimensional discrete-time Gaus- sian 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 proper- ties. 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.

We propose a method for inferring the conditional indepen- dence graph (CIG) of a high-dimensional discrete-time Gaus- sian 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 proper- ties. 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, multi- task learning, nonparametric time series, LASSO

http://publik.tuwien.ac.at/files/PubDat_228799.pdf