Section:
New Results
Sparse covariance inverse estimate for Gaussian Markov Random Field
Participants :
Cyril Furtlehner, Jean-Marc Lasgouttes.
We investigate the problem of Gaussian Markov random field selection
under a non-analytic constraint: the estimated models must be
compatible with a fast inference algorithm, namely the Gaussian belief
propagation algorithm. To address this question, we introduce the
-IPS framework, based on iterative proportional scaling, which
incrementally selects candidate links in a greedy manner. Besides its
intrinsic sparsity-inducing ability, this algorithm is flexible enough
to incorporate various spectral constraints, like e.g. walk
summability, and topological constraints, like short loops avoidance.
Experimental tests on various datasets, including traffic data from
San Francisco Bay Area, indicate that this approach can deliver, with
reasonable computational cost, a broad range of efficient inference
models, which are not accessible through penalization with traditional
sparsity-inducing norms.
This work has been presented at ECML/PKDD
2014 [40] . The code for -IPS has been
made available at https://who.rocq.inria.fr/Jean-Marc.Lasgouttes/star-ips/ .
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