2 research outputs found
Dimensionally Distributed Learning: Models and Algorithm
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.Comment: Proceedings of the Eleventh International Conference on Information
Fusion, Cologne, Germany, June 30 - July 3, 200
Dimensionally Distributed Learning Models and Algorithm
Abstract β 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