Bayesian robust simplicial mixture model

MatLab object for clustering real-valued data with noise, outliers and missing values

The BRSMM class implements algorithms for simulating and estimating the parameters of a finite, simplicial mixture model. Simplicial models, such as the latent Dirichlet allocation (LDA) model, are typically used in text-based information retrieval, e.g. when designating topics for each document within a corpus based on their word statistics. The BRSMM class is an extension of LDA to continuous data. It is specifically designed for data containing outliers and/or missing values.

A BRSMM object models each topic as a mixture of heavy-tailed distributions with topic-specific parameters. The parameters are equipped with a conjugate prior distribution as per the Bayesian paradigm. The model also contains hidden variables representing the missing values in the data and the quality of the data. The posterior distributions over both parameters and hidden variables are estimated by an approximate variational inference algorithm.

This submission includes a test function that generates a set of synthetic data and learns a model from these data. The test function also plots the data clustered according to the model, and the variational lower bound on the marginal log-likelihood of the data after each iteration.

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INSTRUCTIONS:

After downloading this submission, extract the compressed file inside your MatLab working directory and run the test function (brsmmtest.m) for a demonstration.