NIPS2009_0023_slide - Results: an l 1-norm penalty for...

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Sparse and Locally Constant Gaussian Graphical Models M3 Jean Honorio, Luis Ortiz, Dimitris Samaras, Nikos Paragios, Rita Goldstein Motivation: learning GGMs on spatial datasets, e.g.: silhouettes, motion trajectories, 2D and 3D images, with spatial coherence of the dependence/independence relationships (local constancy)
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Unformatted text preview: Results: an l 1-norm penalty for local constancy in a strictly convex maximum likelihood estimation, and an efficient algorithm that decomposes the original optimization problem into a sequence of non-smooth piecewise quadratic problems with closed form solutions walking sequence cardiac MRI brain fMRI...
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This note was uploaded on 02/12/2010 for the course COMPUTER S 10586 taught by Professor Jilinwang during the Fall '09 term at Zhejiang University.

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