In many applications, observations are naturally grouped into different classes.

Fortunately, in many realistic scenarios, only a handful of features may be relevant in distinguishing clusters.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper proposes a new approach for Mahalanobis metric learning for k-nearest neighbor (kNN) classification. The main difference from the existing work is in the way how k nearest neighbors are found. Instead of simply looking for the k nearest neighbors, the authors are searching for the closest k examples that also guarantee correct classification (the authors refer to it as the gerrymandering). They propose a greedy algorithm to find such a neighborhood.