Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data.

ABSTRACT: Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we

SUBMITTER: Kopf A 

PROVIDER: S-EPMC8277074 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature