Ordinal Least Squares Support Vector Machines - A Discriminant Analysis Approach
Kristiaan Pelckmans, Peter Karsmakers, Johan A. K. Suykens · 2006
This paper explores the extension of the classical ideas behind linear discriminant analysis to the problem of ordinal regression. It is shown how this reasoning fits in a framework of least squares support vector machines (LS-SVMs) and kernel machines, hereby allowing for a nonlinear extension. The resulting method is conceived as a practical alternative to proposed, computationally demanding formulations based on maximal margin.