Dimensionally Distributed Learning: Models and Algorithm

Details Dimensionally Distributed Learning: Models and Algorithm Dimensionally Distributed Learning: Models and Algorithm

2008-07-18

arxiv.org/abs/0807.3050v1

Computer Science

Information Theory

Proceedings of the Eleventh International Conference on Information Fusion, Cologne, Germany, June 30 - July 3, 2008

Scientific paper

This paper introduces a framework for regression with dimensionally distributed data with a fusion center. A cooperative learning algorithm, the iterative conditional expectation algorithm (ICEA), is designed within this framework. The algorithm can effectively discover linear combinations of individual estimators trained by each agent without transferring and storing large amount of data amongst the agents and the fusion center. The convergence of ICEA is explored. Specifically, for a two agent system, each complete round of ICEA is guaranteed to be a non-expansive map on the function space of each agent. The advantages and limitations of ICEA are also discussed for data sets with various distributions and various hidden rules. Moreover, several techniques are also designed to leverage the algorithm to effectively learn more complex hidden rules that are not linearly decomposable.

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