  Continuous monitoring with an ever-increasing number of sensors has become
ubiquitous across many application domains. However, acquired time series are
typically high-dimensional and difficult to interpret. Expressive deep learning
(DL) models have gained popularity for dimensionality reduction, but the
resulting latent space often remains difficult to interpret. In this work we
propose SOM-CPC, a model that visualizes data in an organized 2D manifold,
while preserving higher-dimensional information. We address a largely
unexplored and challenging set of scenarios comprising high-rate time series,
and show on both synthetic and real-life data (physiological data and audio
recordings) that SOM-CPC outperforms strong baselines like DL-based feature
extraction, followed by conventional dimensionality reduction techniques, and
models that jointly optimize a DL model and a Self-Organizing Map (SOM).
SOM-CPC has great potential to acquire a better understanding of latent
patterns in high-rate data streams.
