Dimensionally distributed learning models and algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th International Conference on Information Fusion, FUSION 2008
DOIs
StatePublished - Dec 1 2008
Event11th International Conference on Information Fusion, FUSION 2008 - Cologne, Germany
Duration: Jun 30 2008Jul 3 2008

Publication series

NameProceedings of the 11th International Conference on Information Fusion, FUSION 2008

Other

Other11th International Conference on Information Fusion, FUSION 2008
CountryGermany
CityCologne
Period6/30/087/3/08

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

Keywords

  • Distributed learning
  • Estimation
  • Heterogeneous data
  • Regression

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  • Cite this

    Zheng, H., Kulkarni, S. R., & Poor, H. V. (2008). Dimensionally distributed learning models and algorithm. In Proceedings of the 11th International Conference on Information Fusion, FUSION 2008 [4632362] (Proceedings of the 11th International Conference on Information Fusion, FUSION 2008). https://doi.org/10.1109/ICIF.2008.4632362