Relative nearest neighbors for classification

Classification approaches based on the k nearest neighbors are simple and often result in goodxii performance. However, they heavily depend on the collection of selected neighbors. When performing the classification on the sparse or noisy data, the selected nearest neighbors are not consistent with our perception which in turn leads to the worse performance. This paper proposes two new classifiers by applying the relative transformation to define the k nearest neighbors, where the relative transformation is defined on the local region varying with the query sample to generate the relative space in which nearest neighbors to the query sample can be selected more reason-ably. The conducted experiments on challenging benchmark data sets validate the proposed approach.