Pattern Recognition Letters

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Abstract

Nearest neighbour estimators of the order derivatives of the probability density function are introduced. We establish their squared error consistency, and most importantly for data analysis, an automatic, single pass normal scale or ‘rule of thumb’ selector of the number of nearest neighbours. Density derivatives are crucial components for statistical unsupervised learning based on density gradient ascent known as mean shift clustering. The proposed automatic choice of the nearest neighbours for density gradients is applied to the mean shift clustering and is demonstrated to discover accurately the number, location and shape of non-ellipsoidal clusters in multivariate data analysis and image segmentation.