A0173
Title: Expectation-maximization algorithm with combinatorial assumption
Authors: Loc Nguyen - Loc Nguyen's Academic Network (Vietnam) [presenting]
Abstract: The expectation-maximization (EM) algorithm is a popular and powerful mathematical method for parameter estimation in case there exist both observed data and hidden data. The EM process depends on an implicit relationship between observed data and hidden data which is specified by a mapping function in traditional EM and a joint probability density function (PDF) in practical EM. However, the mapping function is vague and impractical whereas the joint PDF is not easy to be defined because of heterogeneity between observed data and hidden data. The aim is to improve the competency of EM by making it more feasible and easier to be specified, which removes the vagueness. Therefore, it is assumed that observed data is the combination of hidden data which is realized as an analytic function where data points are numerical. In other words, observed points are supposedly calculated from hidden points via a regression model. Mathematical computations and proofs indicate the feasibility and clearness of the proposed method which can be considered an extension of EM.
