Clustering based on Mixtures of Sparse Gaussian Processes
This is an incremental improvement for machine learning practitioners needing probabilistic clustering with dimensionality reduction.
The paper tackles the problem of jointly performing clustering and dimensionality reduction by proposing Sparse Gaussian Process Mixture Clustering (SGP-MIC), a probabilistic model based on mixtures of sparse Gaussian processes, which offers advantages over deterministic methods and includes efficient approximations for speed.
Creating low dimensional representations of a high dimensional data set is an important component in many machine learning applications. How to cluster data using their low dimensional embedded space is still a challenging problem in machine learning. In this article, we focus on proposing a joint formulation for both clustering and dimensionality reduction. When a probabilistic model is desired, one possible solution is to use the mixture models in which both cluster indicator and low dimensional space are learned. Our algorithm is based on a mixture of sparse Gaussian processes, which is called Sparse Gaussian Process Mixture Clustering (SGP-MIC). The main advantages to our approach over existing methods are that the probabilistic nature of this model provides more advantages over existing deterministic methods, it is straightforward to construct non-linear generalizations of the model, and applying a sparse model and an efficient variational EM approximation help to speed up the algorithm.