Generalized eigenvalue proximal support vector regressor

Authors:
Reshma Khemchandani;Anuj Karpatne;Suresh Chandra
Affiliations:
Algorithmic Trading Group, Technology Services India, RBS, Gurgaon, Haryana 122022, India;Department of Mathematics, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India;Department of Mathematics, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In this paper, we propose a new non-parallel plane based regressor termed as Generalized Eigenvalue Proximal Support Vector Regressor (GEPSVR). The GEPSVR formulation is in the spirit of non-parallel plane proximal SVMs via generalized eigenvalues and is obtained by solving two generalized eigenvalue problems. Further, an improvement over GEPSVR is proposed that employs a regularization technique, similar to the one proposed in Guarracino, Cifarelli, Seref, and Pardalos (2007), which requires the solution of a single regularized eigenvalue problem only. This regressor has been termed as Regularized GEPSVR (ReGEPSVR). On several benchmark datasets and artificially generated datasets, ReGEPSVR is not only fast, but also shows good generalization when compared with other regression algorithms. It also finds its application in financial time-series forecasting, as shown over financial datasets.