SAP HANA Predictive Analysis Library (PAL)

3.1.6 Gaussian Mixture Model (GMM)
GMM is a Gaussian mixture model in which each component has its own weight, mean, and covariance matrix.
Weight means the importance of a Gaussian distribution in the GMM, and mean and covariance matrix are the
basic parameters of a Gaussian distribution, as shown in the following formula:
Expectation maximization (EM) algorithm is used to inference all of the unknown parameters of GMM. The
algorithm performs two steps: the expectation step and the maximization step.
The expectation step calculates the contribution of training sample i to the Gaussian k:
The maximization step calculates the parameters weight, mean, and covariance matrix:
GMM can be used in image segmentation, clustering, and so on. It gives the probability of a sample belonging
to each Gaussian component.
Prerequisite
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No missing or null data in the inputs
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 53