Title :
Ordinal Least Squares Support Vector Machines - A Discriminant Analysis Approach
Author :
Pelckmans, Kristiaan ; Karsmakers, Peter ; Suykens, Johan A K ; De Moor, Bart
Author_Institution :
ESAT/SCD-SISTA, Katholieke Univ. Leuven, Leuven
Abstract :
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.
Keywords :
least squares approximations; regression analysis; support vector machines; LS-SVM; kernel machine; least squares support vector machines; linear discriminant analysis; maximal margin; nonlinear extension; ordinal regression; Collaboration; Function approximation; Gaussian processes; Information filtering; Information retrieval; Kernel; Least squares methods; Linear discriminant analysis; Support vector machine classification; Support vector machines;
Conference_Titel :
Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on
Conference_Location :
Arlington, VA
Print_ISBN :
1-4244-0656-0
Electronic_ISBN :
1551-2541
DOI :
10.1109/MLSP.2006.275556
