Efficient EM Training of Gaussian Mixtures with Missing Data

Abstract

In data-mining applications, we are frequently faced with a large fraction of missing entries in the data matrix, which is problematic for most discriminant machine learning algorithms. A solution that we explore in this paper is the use of a generative model (a mixture of Gaussians) to compute the conditional expectation of the missing variables given the observed variables. Since training a Gaussian mixture with many different patterns of missing values can be computationally very expensive, we introduce a spanning-tree based algorithm that significantly speeds up training in these conditions. We also observe that good results can be obtained by using the generative model to fill-in the missing values for a separate discriminant learning algorithm.

Cited by in corpus (5)

• A Robust and Flexible EM Algorithm for Mixtures of Elliptical Distributions with Missing Data
• Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension
• Handling missing data in a neural network approach for the identification of charged particles in a multilayer detector
• Variational Auto-Decoder: A Method for Neural Generative Modeling from Incomplete Data
• Learning from Incomplete Features by Simultaneous Training of Neural Networks and Sparse Coding