Adaptive Learning Rate for Online Linear Discriminant Classifiers

Authors:
Ludmila I. Kuncheva;Catrin O. Plumpton
Affiliations:
School of Computer Science, Bangor University, Bangor Gwynedd, UK LL57 1UT;School of Computer Science, Bangor University, Bangor Gwynedd, UK LL57 1UT
Venue:
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2008

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Abstract

We propose a strategy for updating the learning rate parameter of online linear classifiers for streaming data with concept drift. The change in the learning rate is guided by the change in a running estimate of the classification error. In addition, we propose an online version of the standard linear discriminant classifier (O-LDC) in which the inverse of the common covariance matrix is updated using the Sherman-Morrison-Woodbury formula. The adaptive learning rate was applied to four online linear classifier models on generated and real streaming data with concept drift. O-LDC was found to be better than balanced Winnow, the perceptron and a recently proposed online linear discriminant analysis.