Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models

Statistics – Machine Learning

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

34 pages, 6 figures, technical report (submitted)

Scientific paper

Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or super-resolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher-order Bayesian decision-making problems, such as optimizing image acquisition in magnetic resonance scanners, can be addressed by querying the SLM posterior covariance, unrelated to the density's mode. We propose a scalable algorithmic framework, with which SLM posteriors over full, high-resolution images can be approximated for the first time, solving a variational optimization problem which is convex iff posterior mode finding is convex. These methods successfully drive the optimization of sampling trajectories for real-world magnetic resonance imaging through Bayesian experimental design, which has not been attempted before. Our methodology provides new insight into similarities and differences between sparse reconstruction and approximate Bayesian inference, and has important implications for compressive sensing of real-world images.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models 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 Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-205473

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.