NDLI: Dimensionally distributed learning models and algorithm

Content Provider	IEEE Xplore Digital Library
Author	Haipeng Zheng Kulkarni, S.R. Poor, H.V.
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. Eng., Princeton Univ., Princeton, NJ (Haipeng Zheng; Kulkarni, S.R.; Poor, H.V.)
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.
Starting Page	1
Ending Page	8
File Size	3122049
Page Count	8
File Format	PDF
ISBN	9783800730926
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-30
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	ISIF (Intl Society of Information Fusion)
Subject Keyword	estimation Distributed learning heterogeneous data regression
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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