Expectation-maximization estimators for incompletely observed data

Data Synthesis with Expectation-Maximization

A problem of increasing importance in computer graphics is to generate data with the style of some previous training data, but satisfying new constraints. If we use a probabilistic latent variable model, then learning the model will normally be performed using Expectation-Maximization (EM), or one of its generalizations. We show that data synthesis for such problems can also be performed using ...

Expectation Maximization for Weakly Labeled Data

We call data weakly labeled if it has no exact label but rather a numerical indication of correctness of the label “guessed” by the learning algorithm a situation commonly encountered in problems of reinforcement learning. The term emphasizes similarities of our approach to the known techniques of solving unsupervised and transductive problems. In this paper we present an on-line algorithm that...

Aerial Lidar Data Classification using Expectation-Maximization

We use the Expectation-Maximization (EM) algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged)...

Expectation Maximization

Expectation Maximization

The Expectation Maximization (EM) algorithm [1, 2] is one of the most widely used algorithms in statistics. Suppose we are given some observed data X and a model family parametrized by θ, and would like to find the θ which maximizes p(X |θ), i.e. the maximum likelihood estimator. The basic idea of EM is actually quite simple: when direct maximization of p(X |θ) is complicated we can augment the...