Maximum Entropy Discrimination Mixtures of Gaussians (Medmix)


MATLAB Machine Learning Toolbox (MaLT)


Nonstationary Kernel Selection

The following example illustrates the effect of nonstationary kernel selection. A nonstationary kernel depends upon local information about the input space. This allows for a very general and powerful representation. However, the technique introduces greater risk of overfitting.

Large Margin Ratio of Gaussian Mixtures

This visualization uses synthetic data to illustrate the idea of large margin discrimination using a ratio of mixture models. We chose 40 examples from eight Gaussian clusters that are interleaved vertically with respect to class label. This example is extreme, because, in effect, a variable has been introduced that has no bearing on correct classification. We use a ratio of two mixture models, each with two identity covariance Gaussian components per class, to classify the data.

Iterative optimizer

We have written an iterative axis-parallel smo-like optimizer to solve the MED optimization. Timing tests were performed with a series of small data sets. Comparison is against the matlab quadprog.

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