Density estimation using modified expectation-maximization algorithm for a linear combination of Gaussians

In this paper we present a new approach for density estimation. The proposed approach is based on modifying expectation-maximization (EM) algorithm to approximate an empirical probability density function of scalar data with a linear combination of Gaussians (LCG). We also propose a novel EM-based sequential technique to get a close initial LCG approximation the modified EM algorithm should start with. Due to both positive and negative components, the LCG approximates inter-class transitions more accurately than a conventional mixture of only positive Gaussians. Experiments on simulated images demonstrate the accuracy of our approach.