Title: Inferring Principal Components in the Simplex with Multinomial Variational Autoencoders. Abstract: 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, due to measurement and instrument constraints. In compositional settings, three key factors limit the ability of these models to estimate covariance: (1) the computational complexity of inverting high-dimensional covariance matrices, (2) the non-exchangeability introduced from the summation constraint on multinomial parameters, and (3) the irreducibility of the component multinomial logistic normal distribution that necessitates the use of parameter augmentation, or similar techniques, during inference. We show that a variational autoencoder augmented with a fast isometric log-ratio (ILR) transform can address these issues and accurately estimate principal components from multinomially logistic normal distributed data.
This model can be optimized on GPUs and modified to handle mini-batching, with the ability to scale across thousands of dimensions and thousands of samples.