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Clustering Based on Predictive Variances in Gaussian Process Regression Models

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We use predictive variances in Gaussian process regression model for clustering. The predictive variances in Gaussian processes learned from a training data are shown to comprise an estimate of the support of a probability density function. The constructed variance function is then applied to construct a set of contours that enclose the data points, which correspond to cluster boundaries. To perform clustering tasks of the data points, an associated dynamical system is built, and its topological invariant property is investigated. The experimental results show that the proposed method works successfully for clustering problems with arbitrary shapes.

This talk is part of the Machine Learning @ CUED series.

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