Incremental and Decremental Least Squares Support Vector Machine and Its Application to Drug Design

Authors:
Hyunsoo Kim;Haesun Park
Affiliations:
University of Minnesota - Twin Cities;University of Minnesota - Twin Cities
Venue:
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Year:
2004

Citing 2
Cited 2

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Matrix computations (3rd ed.)
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Decision Support Systems

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Abstract

The least squares support vector machine (LS-SVM) has shown to exhibit excellent classification performance in many applications. In this paper, we propose an incremental and decremental LS-SVM based on updating and downdating the QR decomposition. It can efficiently compute the updated solution when data points are appended or removed. The experiment results illustrated that the proposed incremental algorithm efficiently produces the same solutions as those obtained by LS-SVM which recomputes the solution all over even for small changes in the data. For drug design, the results of each biochemistry laboratory test on a new compound can be iteratively included in the training set. This procedure can further improve precision in order to select the next best predicted organic compound. Instead of retraining entire data points, it is much efficient to update solution by incremental LS-SVM.