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We propose that the square root of a probability density function can be represented as a linear combination of Gaussian kernels. It is shown that if the Gaussian kernel centres and kernel width are known, then the maximum likelihood parameter estimator can be formulated as a Riemannian optimisation problem on sphere manifold. The first order Riemannian geometry of the sphere manifold and vector transport...
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