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Variational Bayesian Relevance Vector Machine for Sparse Coding

version (2.84 KB) by Mo Chen
Variational Bayesian Relevance Vector Machine for Sparse Coding


Updated 13 Mar 2016

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Compressive sensing or sparse coding is to learn sparse representation of data. The simplest method is to use linear regression with L1 regularization. While this package provides Bayesian treatment for sparse coding problems. It uses variational Bayesian to train the model.
The sparse coding problem is modeled as linear regression with a sparse prior (automatic relevance determination, ARD), which is also known as Relevance Vector Machine (RVM). The advantage is that it can do model selection automatically. As a result, this is no need to mannully specify the regularization parameter (learned from data) and better sparse recovery can be obtained. Please run the demo script in the package to give it a try.

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MATLAB Release Compatibility
Created with R2016a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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