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On Spectral Learning of Mixtures of Distributions
[chapter]
2005
Lecture Notes in Computer Science
We consider the problem of learning mixtures of distributions via spectral methods and derive a tight characterization of when such methods are useful. Specifically, given a mixture-sample, let µ i , C i , w i denote the empirical mean, covariance matrix, and mixing weight of the i-th component. We prove that a very simple algorithm, namely spectral projection followed by single-linkage clustering, properly classifies every point in the sample when each µ i is separated from all µ j by C i 2
doi:10.1007/11503415_31
fatcat:73dbu4fv4jdqzdhx2gwhejlvrq