Distributed Kernel Regression: An Algorithm for Training Collaboratively

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.

To be presented at the 2006 IEEE Information Theory Workshop, Punta del Este, Uruguay, March 13-17, 2006

References in corpus (2)

• Consistency in Models for Distributed Learning under Communication Constraints
• Distributed Regression in Sensor Networks: Training Distributively with Alternating Projections

Cited by in corpus (5)

• Distributed Learning in Wireless Sensor Networks
• COKE: Communication-Censored Decentralized Kernel Learning
• Scalable Algorithms for Aggregating Disparate Forecasts of Probability
• Collaborative Training in Sensor Networks: A graphical model approach
• Real-time semiparametric regression for distributed data sets