1 Adaptive Metric Learning Vector Quantization for Ordinal Classification Shereen Fouad and Peter Tino 1 1 School of Computer Science, The University of Birmingham, Birmingham B15 2TT, United Kingdom, (e-mail: saf942, P.Tino@cs.bham.ac.uk.) Keywords: Generalized Matrix Learning Vector Quantization (GMLVQ), Matrix Learning Vector Quantization (MLVQ), Ordinal Classification. Abstract Many pattern analysis problems require classification of examples into naturally or- dered classes. In such cases nominal classification schemes will ignore the class order relationships, which can have detrimental effect on classification accuracy. This paper introduces two novel ordinal Learning Vector Quantization (LVQ) schemes, with metric learning, specifically designed for classifying data items into ordered classes. Unlike in nominal LVQ, in ordinal LVQ the class order information is utilized during training in selection of the class prototypes to be adapted, as well as in determining the exact man- ner in which the prototypes get updated. Prototype based models are in general more amenable to interpretations and can often be constructed at a smaller computational cost than alternative non-linear classification models. Experiments demonstrate that the pro- posed ordinal LVQ formulations compare favourably with their nominal counterparts. Moreover, our methods achieve competitive performance against existing benchmark ordinal regression models.