Fast multi-output relevance vector regression
This work addresses a computational bottleneck for researchers and practitioners using multi-output regression, though it is incremental as it builds on existing relevance vector regression methods.
The paper tackles the high time complexity of multi-output relevance vector regression by reducing it from O(VM^3) to O(V^3+M^3), where V<M, and demonstrates that the proposed method is more competitive in computation time than existing approaches.
This paper aims to decrease the time complexity of multi-output relevance vector regression from O(VM^3) to O(V^3+M^3), where V is the number of output dimensions, M is the number of basis functions, and V<M. The experimental results demonstrate that the proposed method is more competitive than the existing method, with regard to computation time. MATLAB codes are available at http://www.mathworks.com/matlabcentral/fileexchange/49131.