Approximate UMAP allows for high-rate online visualization of
  high-dimensional data streams
Peter Wassenaar Pierre Guetschel Michael Tangermann
In the BCI field, introspection and interpretation of brain signals are
desired for providing feedback or to guide rapid paradigm prototyping but are
challenging due to the high noise level and dimensionality of the signals. Deep
neural networks are often introspected by transforming their learned feature
representations into 2- or 3-dimensional subspace visualizations using
projection algorithms like Uniform Manifold Approximation and Projection
(UMAP). Unfortunately, these methods are computationally expensive, making the
projection of data streams in real-time a non-trivial task. In this study, we
introduce a novel variant of UMAP, called approximate UMAP (aUMAP). It aims at
generating rapid projections for real-time introspection. To study its
suitability for real-time projecting, we benchmark the methods against standard
UMAP and its neural network counterpart parametric UMAP. Our results show that
approximate UMAP delivers projections that replicate the projection space of
standard UMAP while decreasing projection speed by an order of magnitude and
maintaining the same training time.