{
  "id": "2003.03462",
  "version": "v1",
  "published": "2020-03-06T23:10:52.000Z",
  "updated": "2020-03-06T23:10:52.000Z",
  "title": "BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders",
  "authors": [
    "Kaspar Märtens",
    "Christopher Yau"
  ],
  "comment": "Accepted to AISTATS 2020",
  "categories": [
    "stat.ML",
    "cs.LG",
    "q-bio.GN"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-03-06T23:10:52.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "variational autoencoders",
      "translation-invariant feature-level clustering",
      "dimensionality reduction",
      "handle translation-invariant basis functions",
      "single-cell gene expression data"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}