Variational Bayesian Relevance Vector Machine for Sparse Coding

버전 1.0.0.0 (2.84 KB) 작성자: Mo Chen
Variational Bayesian Relevance Vector Machine for Sparse Coding

다운로드 수: 661

업데이트 날짜: 2016/3/13

라이선스 보기

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.

인용 양식

Mo Chen (2023). Variational Bayesian Relevance Vector Machine for Sparse Coding (https://www.mathworks.com/matlabcentral/fileexchange/55948-variational-bayesian-relevance-vector-machine-for-sparse-coding), MATLAB Central File Exchange. 검색됨 .

MATLAB 릴리스 호환 정보
개발 환경: R2016a
모든 릴리스와 호환
플랫폼 호환성
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
버전 게시됨 릴리스 정보
1.0.0.0