Locally Weighted Projection Regression (LWPR) is a fully incremental, online algorithm for non-linear function approximation in high dimensional spaces, capable of handling redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. Please cite:
[1] Sethu Vijayakumar, Aaron D'Souza and Stefan Schaal, Incremental Online Learning in High Dimensions, Neural Computation, vol. 17, no. 12, pp. 2602-2634 (2005).
[2] Stefan Klanke, Sethu Vijayakumar and Stefan Schaal, A Library for Locally Weighted Projection Regression, Journal of Machine Learning Research (JMLR), vol. 9, pp. 623--626 (2008).
More details and usage guidelines on the code website.

Project Activity

See All Activity >

Follow LWPR

LWPR Web Site

Other Useful Business Software

Communicate & Connect with Ring Central's VoIP Solution Communicate & Connect with Ring Central's VoIP Solution Icon
Communicate & Connect with Ring Central's VoIP Solution Icon

Cloud Powered Business Phone System

  • Unrivaled value & reliability in one solution
  • Unlimited Calls/SMS/Conferencing/Fax
  • Trusted by 350,000+ Businesses

Rate This Project

Login To Rate This Project

User Reviews

Be the first to post a review of LWPR!

Additional Project Details

Programming Language

C, MATLAB, Python

Registered

2012-01-30