Design of Blurring Mean-Shift Algorithms for Data Classification |
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Authors: | Carlo Grillenzoni |
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Institution: | 1.IUAV University,Venice,Italy;2.Institute of Architecture,University of Venice,Venezia,Italy |
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Abstract: | The mean-shift algorithm is an iterative method of mode seeking and data clustering based on the kernel density estimator. The blurring mean-shift is an accelerated version which uses the original data only in the first step, then re-smoothes previous estimates. It converges to local centroids, but may suffer from problems of asymptotic bias, which fundamentally depend on the design of its smoothing components. This paper develops nearest-neighbor implementations and data-driven techniques of bandwidth selection, which enhance the clustering performance of the blurring method. These solutions can be applied to the whole class of mean-shift algorithms, including the iterative local mean method. Extended simulation experiments and applications to well known data-sets show the goodness of the blurring estimator with respect to other algorithms. |
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