A performance comparison of RIPPLE and LMS for high dimensional piecewise linear functions

Authors:
Manjula A. Iyer;Layne T. Watson
Affiliations:
Virginia Polytechnic Institute and State University, Blacksburg, Virginia;Virginia Polytechnic Institute and State University, Blacksburg, Virginia
Venue:
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
Year:
2007

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Abstract

The method of ordinary least squares approximation is not resistant to data points that cause a disproportionate influence in the fit. When outliers are known to exist in the data, robust estimation algorithms are preferred. However, the performance of most robust estimation algorithms degrades in higher dimensions due to factorial complexity and sparse data. A new polynomial time algorithm RIPPLE has been developed to produce robust estimations of data obtained from piecewise linear functions. This paper presents a comparison between the new algorithm RIPPLE and a standard robust estimation algorithm LMS.