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Register a new userLower Bounds on the Total Variation Distance Between Mixtures of Two Gaussians
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Added by
Soumyabrata Pal
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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No Arabic abstract
Mixtures of high dimensional Gaussian distributions have been studied extensively in statistics and learning theory. While the total variation distance appears naturally in the sample complexity of distribution learning, it is analytically difficult to obtain tight lower bounds for mixtures. Exploiting a connection between total variation distance and the characteristic function of the mixture, we provide fairly tight functional approximations. This enables us to derive new lower bounds on the total variation distance between pairs of two-component Gaussian mixtures that have a shared covariance matrix.
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