Nonparametric Nearest Neighbor Random Process Clustering

20 Apr 2015 Michael Tschannen Helmut Bölcskei

We consider the problem of clustering noisy finite-length observations of stationary ergodic random processes according to their nonparametric generative models without prior knowledge of the model statistics and the number of generative models. Two algorithms, both using the L1-distance between estimated power spectral densities (PSDs) as a measure of dissimilarity, are analyzed... (read more)

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