December 2018
Beginner to intermediate
684 pages
21h 9m
English
Content preview from Hands-On Machine Learning for Algorithmic Trading
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The expectation-maximization algorithm
GMM uses the expectation-maximization algorithm to identify the components of the mixture of Gaussian distributions. The goal is to learn the probability distribution parameters from unlabeled data.
The algorithm proceeds iteratively as follows:
- Initialization—Assume random centroids (for example, using k-Means)
- Repeat the following steps until convergence (that is, changes in assignments drop below the threshold):
- Expectation step: Soft assignment—compute probabilities for each point from each distribution
- Maximization step: Adjust normal-distribution parameters to make data points most likely
The following screenshot shows the GMM cluster membership probabilities for the Iris dataset as contour ...
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