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Kanishka Bhaduri

Member since: Sep 24, 2010, Mission Critical Technologies Inc

A Scalable Local Algorithm for Distributed Multivariate Regression

Shared by Kanishka Bhaduri, updated on Sep 22, 2010

Summary

Author(s) :
Kanishka Bhaduri, H. Kargupta
Abstract

This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm can be used for distributed inferencing, data compaction, data modeling and classification tasks in many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Computing a global regression model from data available at the different peer-nodes using a traditional centralized algorithm for regression can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. This paper proposes a two-step approach to deal with this problem. First, it offers an efficient local distributed algorithm that monitors the quality of the current regression model. If the model is outdated, it uses this algorithm as a feedback mechanism for rebuilding the model. The local nature of the monitoring algorithm guarantees low monitoring cost. Experimental results presented in this paper strongly support the theoretical claims.

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Publication Name
A Scalable Local Algorithm for Distributed Multivariate Regression
Publication Location
Statistical Analysis and Data Mining 1(3): 177-194
Year Published
2008

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