Distributed Kernel Regression: An Algorithm for Training Collaboratively
Machine Learning
2016-11-15 v1 Artificial Intelligence
Distributed, Parallel, and Cluster Computing
Information Theory
math.IT
Abstract
This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized kernel least-squares regression estimators is derived. Noting that the algorithm can be viewed as an application of successive orthogonal projection algorithms, its convergence properties are investigated and the statistical behavior of the estimator is discussed in a simplified theoretical setting.
Cite
@article{arxiv.cs/0601089,
title = {Distributed Kernel Regression: An Algorithm for Training Collaboratively},
author = {Joel B. Predd and Sanjeev R. Kulkarni and H. Vincent Poor},
journal= {arXiv preprint arXiv:cs/0601089},
year = {2016}
}
Comments
To be presented at the 2006 IEEE Information Theory Workshop, Punta del Este, Uruguay, March 13-17, 2006