Expectation Maximization Machine Learning Tools

The EM algorithm is an iterative method that estimates parameters for statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

The package includes the machine definition per se and a selection of different trainers for specialized purposes:

• Maximum Likelihood (ML)
• Maximum a Posteriori (MAP)
• K-Means
• Inter Session Variability Modelling (ISV)
• Joint Factor Analysis (JFA)
• Total Variability Modeling (iVectors)
• Probabilistic Linear Discriminant Analysis (PLDA)
• EM Principal Component Analysis (EM-PCA)

Installation

To install this package -- alone or together with other Packages of Bob -- please read the Installation Instructions. For Bob to be able to work properly, some dependent packages are required to be installed. Please make sure that you have read the Dependencies for your operating system.

Documentation

For further documentation on this package, please read the Stable Version or the Latest Version of the documentation. For a list of tutorials on this or the other packages ob Bob, or information on submitting issues, asking questions and starting discussions, please visit its website.