MINLIP: Efficient Learning of Transformation Models

Abstract. This paper studies a risk minimization approach to estimate a transformation model from noisy observations. It is argued that transformation models are a natural candidate to study ranking models and ordinal regression in a context of machine learning. We do implement a structural risk minimization strategy based on a Lipschitz smoothness condition of the transformation model. Then, it is shown how the estimate can be obtained efficiently by solving a convex quadratic program with O(n) linear constraints and unknowns, with n the number of data points. A set of experiments do support these findings. Key words: support vector machines, ranking models, ordinal regression

• Type: Journal
• Year: 2009
• Where: ICANN
• Authors: Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel
• Keywords: ICANN 2009, Neural Networks, Ordinal Regression, Risk Minimization, Transformation Model