Visualizing and Exploring Dynamic High-Dimensional Datasets with
  LION-tSNE
  T-distributed stochastic neighbor embedding (tSNE) is a popular and
prize-winning approach for dimensionality reduction and visualizing
high-dimensional data. However, tSNE is non-parametric: once visualization is
built, tSNE is not designed to incorporate additional data into existing
representation. It highly limits the applicability of tSNE to the scenarios
where data are added or updated over time (like dashboards or series of data
snapshots).
  In this paper we propose, analyze and evaluate LION-tSNE (Local Interpolation
with Outlier coNtrol) - a novel approach for incorporating new data into tSNE
representation. LION-tSNE is based on local interpolation in the vicinity of
training data, outlier detection and a special outlier mapping algorithm. We
show that LION-tSNE method is robust both to outliers and to new samples from
existing clusters. We also discuss multiple possible improvements for special
cases.
  We compare LION-tSNE to a comprehensive list of possible benchmark approaches
that include multiple interpolation techniques, gradient descent for new data,
and neural network approximation.
