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We propose mean-shift to detect outlier points. The method processed every point by calculating its k-nearest neighbors (k-NN), and then shifting the point to the mean of its neighborhood. This is repeated three times. The bigger the movement, the more likely the point is an outlier. Boundary points are expected to move more than inner points; outliers more than boundary. The outlier detection is then a simple thresholding based on standard deviation of all movements. Points that move more than that are detected as outliers. The method outperforms all compared outlier detection methods.
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