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Creating low dimensional representations of a high dimensional data set is animportant component in many machine learning applications. How to cluster datausing their low dimensional embedded space is still a challenging problem inmachine learning. In this article, we focus on proposing a joint formulationfor both clustering and dimensionality reduction. When a probabilistic model isdesired, one possible solution is to use the mixture models in which bothcluster indicator and low dimensional space are learned. Our algorithm is basedon a mixture of sparse Gaussian processes, which is called Sparse GaussianProcess Mixture Clustering (SGP-MIC). The main advantages to our approach overexisting methods are that the probabilistic nature of this model provides moreadvantages over existing deterministic methods, it is straightforward toconstruct non-linear generalizations of the model, and applying a sparse modeland an efficient variational EM approximation help to speed up the algorithm.

Clustering based on Mixtures of Sparse Gaussian Processes