Epanechnikov kernel for incomplete data

The Epanechnikov kernel (EK) is a popular kernel function that has achieved promising results in many machine learning applications. Although the EK is widely used, its basic formulation requires fully observed input feature vectors. A method is proposed to estimate the EK when these input vectors are only partially observed, i.e. some of its features are missing. In the proposed method, named expected EK, the expected value of the kernel function is estimated given the distribution of the data and the observed values of the feature vectors.