The accuracy of the k-nearest neighbor algorithm depends on the distance function used to measure similarity between instances. Methods have been proposed in the literature to learn a good distance function from a labelled training set. One such method is the large margin nearest neighbor classifier that learns a global Mahalanobis distance. We propose a mixture of such classifiers where a gating function divides the input space into regions and a separate distance function is learned in each region in a lower dimensional manifold. We show that such an extension improves accuracy and allows visualization.
展开▼
