Learning k for kNN Classification

The K Nearest Neighbor (kNN) method has widely been used in the applications of data mining and machine learning due to its simple implementation and distinguished performance. However, setting all test data with the same k value in the previous kNN methods has been proven to make these methods impractical in real applications. This article proposes to learn a correlation matrix to reconstruct test data points by training data to assign different k values to different test data points, referred to as the Correlation Matrix kNN (CM-kNN for short) classification. Specifically, the least-squares loss function is employed to minimize the reconstruction error to reconstruct each test data point by all training data…

• NN
  National Natural Science Foundation of China

  Awards: 61573270, 61263035, 61573270 and 61672177, 61672177, 61263035, 2013CB329404
• NS
  Natural Science Foundation of Guangxi Province

  Award: 2015GXNSFCB139011
• NK
  National Key Research and Development Program of China

  Awards: 2013CB329404, 2016YFB1000905