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Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

Volkan Cevher, Anastasios Kyrillidis

The convex

\ell_1

-regularized

\log\det

divergence criterion has been shown to produce theoretically consistent graph learning. However, this objective function is challenging since the

\ell_1

-regularization is nonsmooth, the

\log\det

objective is n ...

2013

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Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

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Fast Proximal algorithms for Self-concordant function minimization with application to sparse graph selection

The Integration of an RE Method and AHP: A Pilot Study in a Large Swiss Bank

Total energy estimation model for remote laser welding process

A Track Creation and Deletion Framework for Long-Term Online Multi-Face Tracking

Distribution-based iterative pairwise classification of emotions in the wild using LGBP-TOP

Grapheme and Multilingual Posterior Features for Under-Resourced Speech Recognition: A Study on Scottish Gaelic

FEATURE AND SCORE LEVEL COMBINATION OF SUBSPACE GAUSSIANS IN LVCSR TASK

treeKL: A distance between high dimension empirical distributions

Detection of Floods In Sar Images with Non-Linear Kernel Clustering and Topographic Prior

Fast Proximal Algorithms For Self-Concordant Function Minimization With Application To Sparse Graph Selection