  When training automated systems, it has been shown to be beneficial to adapt
the representation of data by learning a problem-specific metric. This metric
is global. We extend this idea and, for the widely used family of k nearest
neighbors algorithms, develop a method that allows learning locally adaptive
metrics. These local metrics not only improve performance but are naturally
interpretable. To demonstrate important aspects of how our approach works, we
conduct a number of experiments on synthetic data sets, and we show its
usefulness on real-world benchmark data sets.
