Spectral clustering using PCKID – A probabilistic cluster kernel for incomplete data

Abstract

In this paper, we propose <i>PCKID</i>, a novel, robust, kernel function for spectral clustering, specifically designed to handle incomplete data. By combining posterior distributions of Gaussian Mixture Models for incomplete data on different scales, we are able to learn a kernel for incomplete data that does not depend on any critical hyperparameters, unlike the commonly used RBF kernel. To evaluate our method, we perform experiments on two real datasets. <i>PCKID</i>outperforms the baseline methods for all fractions of missing values and in some cases outperforms the baseline methods with up to 25% points.