Optimization Models for Feature Selection of Decomposed Nearest Neighbor
提出一种分解版K近邻方法，通过凸优化学习距离度量并利用L1正则化自动选择重要特征，在高维分类问题中表现优于传统算法。
The traditional k-nearest neighbor (KNN) method classifies an object by the majority vote of its neighbors, and only one parameter k is to be optimized. We propose a decomposed version of the KKN (DKNN) method, which classifies an unseen object based on the distances to the centroids of the KNNs. DKNN has a training process to learn the local-optimafree distance metric by solving a convex optimization problem. The optimization problem not only learns a metric that minimizes classification errors and maximizes the margin between intraclass and interclass distances. In addition, it also selects important features and removes irrelevant features when L1 regularization is incorporated in the optimization model. The feature selection component is extremely useful for high-dimensional classification problems. Tested on public datasets, the proposed DKNN is competitive and often outperforms traditional machine learning algorithms.