Generative Local Metric Learning for Nadaraya-Watson Kernel Estimation

摘要: In kernel learning, people have been using kernel parameters such as bandwidth in Gaussian for learning representations. In this work, we consider the Nadaraya-Watson (NW) estimation, and we present, instead of selecting a simple kernel parameter, learning a metric is possible from the configuration of data, and it drastically increases the estimation performance.