  Given a small set of labeled data and a large set of unlabeled data,
semi-supervised learning (SSL) attempts to leverage the location of the
unlabeled datapoints in order to create a better classifier than could be
obtained from supervised methods applied to the labeled training set alone.
Effective SSL imposes structural assumptions on the data, e.g. that neighbors
are more likely to share a classification or that the decision boundary lies in
an area of low density. For complex and high-dimensional data, neural networks
can learn feature embeddings to which traditional SSL methods can then be
applied in what we call hybrid methods.
  Previously-developed hybrid methods iterate between refining a latent
representation and performing graph-based SSL on this representation. In this
paper, we introduce a novel hybrid method that instead applies low-density
separation to the embedded features. We describe it in detail and discuss why
low-density separation may be better suited for SSL on neural network-based
embeddings than graph-based algorithms. We validate our method using in-house
customer survey data and compare it to other state-of-the-art learning methods.
Our approach effectively classifies thousands of unlabeled users from a
relatively small number of hand-classified examples.
