Self-representation nearest neighbor search for classification

This paper proposes a self-representation method for kNN (k nearest neighbors) classification. Specifically, this paper first designs a self-reconstruction method to reconstruct each data point by all the data, and the derived reconstruction coefficient is then used for calculating the k value for each training sample. Furthermore, a decision tree is built with the resulting k values for each data point to output labels of the training samples. With the built decision tree, the proposed method classifies test samples. Finally, the experimental results on real datasets showed the proposed method outperformed the state-of-the-art methods.