BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders

• URL: http://arxiv.org/abs/2003.03462v1
• Date: Fri, 6 Mar 2020 23:10:52 GMT
• Title: BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders
• Authors: Kaspar M\"artens and Christopher Yau
• Abstract summary: Variational Autoencoders (VAEs) provide a flexible and scalable framework for non-linear dimensionality reduction. We show how a collapsed variational inference scheme leads to scalable and efficient inference for BasisVAE.
• Score: 9.51828574518325
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational Autoencoders (VAEs) provide a flexible and scalable framework for non-linear dimensionality reduction. However, in application domains such as genomics where data sets are typically tabular and high-dimensional, a black-box approach to dimensionality reduction does not provide sufficient insights. Common data analysis workflows additionally use clustering techniques to identify groups of similar features. This usually leads to a two-stage process, however, it would be desirable to construct a joint modelling framework for simultaneous dimensionality reduction and clustering of features. In this paper, we propose to achieve this through the BasisVAE: a combination of the VAE and a probabilistic clustering prior, which lets us learn a one-hot basis function representation as part of the decoder network. Furthermore, for scenarios where not all features are aligned, we develop an extension to handle translation-invariant basis functions. We show how a collapsed variational inference scheme leads to scalable and efficient inference for BasisVAE, demonstrated on various toy examples as well as on single-cell gene expression data.

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