Multinomial Variational Autoencoders can recover Principal Components

John Doe
Covariance estimation on high dimensional data is a central challenge across multiple scientific disciplines. Sparse high-dimensional count data frequently encountered in biological applications such as DNA sequencing and proteomics are often well modeled using multinomial logistic-normal models.
In many cases these datasets are also compositional, presented item-wise as fractions of a normalized total, necessitated by measurement and instrument constraints. Three key challenges prove limiting in covariance estimation using these models in compositional settings: (1) the...
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