A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso

Details A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso

2011-10-25

arxiv.org/abs/1110.5508v1

Statistics

Machine Learning

Scientific paper

We propose a scalable, efficient and statistically motivated computational framework for Graphical Lasso (Friedman et al., 2007b) - a covariance regularization framework that has received significant attention in the statistics community over the past few years. Existing algorithms have trouble in scaling to dimensions larger than a thousand. Our proposal significantly enhances the state-of-the-art for such moderate sized problems and gracefully scales to larger problems where other algorithms become practically infeasible. This requires a few key new ideas. We operate on the primal problem and use a subtle variation of block-coordinate-methods which drastically reduces the computational complexity by orders of magnitude. We provide rigorous theoretical guarantees on the convergence and complexity of our algorithm and demonstrate the effectiveness of our proposal via experiments. We believe that our framework extends the applicability of Graphical Lasso to large-scale modern applications like bioinformatics, collaborative filtering and social networks, among others.

Affiliated with

Also associated with

No associations

Rating

A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso 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 A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-94709