Sparse and Locally Constant Gaussian Graphical Models

NeurIPS 2009 Jean HonorioDimitris SamarasNikos ParagiosRita GoldsteinLuis E. Ortiz

Locality information is crucial in datasets where each variable corresponds to a measurement in a manifold (silhouettes, motion trajectories, 2D and 3D images). Although these datasets are typically under-sampled and high-dimensional, they often need to be represented with low-complexity statistical models, which are comprised of only the important probabilistic dependencies in the datasets... (read more)

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