Statistics – Machine Learning
Scientific paper
2011-06-25
Statistics
Machine Learning
13 pages
Scientific paper
We study the problem of estimating from data, a sparse approximation to the inverse covariance matrix. Estimating a sparsity constrained inverse covariance matrix is a key component in Gaussian graphical model learning, but one that is numerically very challenging. We address this challenge by developing a new adaptive gradient-based method that carefully combines gradient information with an adaptive step-scaling strategy, which results in a scalable, highly competitive method. Our algorithm, like its predecessors, maximizes an $\ell_1$-norm penalized log-likelihood and has the same per iteration arithmetic complexity as the best methods in its class. Our experiments reveal that our approach outperforms state-of-the-art competitors, often significantly so, for large problems.
Kim Dongmin
Sra Suvrit
No associations
LandOfFree
Sparse Inverse Covariance Estimation via an Adaptive Gradient-Based Method does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Sparse Inverse Covariance Estimation via an Adaptive Gradient-Based Method, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Sparse Inverse Covariance Estimation via an Adaptive Gradient-Based Method will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-585216