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Estimating the scale parameter for Quantum Clustering

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If you have a question about this talk, please contact Per Kristian Lehre.

After introducing the quantum clustering as a probability density estimation based method, we address the problem of scale estimation. The scale is estimated using a Bayesian approach when assuming a Gamma prior. The Gamma distribution is evaluated from the data set itself. The scale is applied to three different machine learning algorithms mainly used for data segmentation: scale-space, mean shift and quantum clustering. The proposed approach is applied to modulated signal classification and terrain segmentation using distributions of surface normal orientations.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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