Hierarchical Large Margin Nearest Neighbor Classification

Distance metric learning has exhibited its great power to enhance performance in metric related pattern recognition tasks. The recent large margin nearest neighbor classification (LMNN) improves the performance of k-nearest neighbor classification by learning a global distance metric. However, it does not consider the locality of data distributions, which is crucial in determining a proper metric. In this paper, we propose a novel local distance metric learning method called hierarchical LMNN (HLMNN) which first builds a hierarchical structure by grouping data points according to the overlapping ratios defined by us and then learns distance metrics sequentially. Experimental results on real-world data sets including comparisons with the traditional k-nearest neighbor and the state-of-the-art LMNN show the effectiveness of the proposed HLMNN.