Mean Shift Algorithm for Exponential Families with Applications to Speaker Clustering
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This work extends the mean shift algorithm from the observation space to the manifolds of parametric models that are formed by exponential families. We show how the Kullback-Leibler divergence and its dual define the corresponding affine connection and propose a method for incorporating the uncertainty in estimating the parameters. Experiments are carried out for the problem of speaker clustering, using both single Gaussians and i-vectors.
