Abstract
Ordinal regression addresses the problem of predicting non-numerical ordered classes. It walks a fine line between standard regression and classification, and the problem is often addressed from one of these perspectives. This can lead to suboptimal results as the ordinal information in the data may not be properly exploited. In this work we propose a distance metric learning algorithm to handle ordinal regression. Our model aims at optimizing the number of ordered sequences in local neighborhoods of the data, so that the learned distance can then be used by a distance-based predictor and improve its performance in ordinal regression problems. We evaluate our algorithm on several ordinal regression datasets and show that it outperforms the current distance metric learning for ordinal regression proposals, as well as being competitive with respect to the state-of-the-art of ordinal regression. The current paper is an extended abstract for the work [1].