Euclidean distance estimation in incomplete datasets

This paper proposes a method to estimate the expected value of the Euclidean distance between two possibly incomplete feature vectors. Under the Missing at Random assumption, we show that the Euclidean distance can be modeled by a Nakagami distribution, for which the parameters we express as a function of the moments of the unknown data distribution. In our formulation the data distribution is modeled using a mixture of Gaussians. The proposed method, named Expected Euclidean Distance (EED), is validated through a series of experiments using synthetic and real-world data. Additionally, we show the application of EED to the Minimal Learning Machine (MLM), a distance-based supervised learning method. Experimental results show that EED outperforms existing methods that estimate Euclidean distances in an indirect manner. We also observe that the application of EED to the MLM provides promising results.