  The performance of machine learning model can be further improved if
contextual cues are provided as input along with base features that are
directly related to an inference task. In offline learning, one can inspect
historical training data to identify contextual clusters either through feature
clustering, or hand-crafting additional features to describe a context. While
offline training enjoys the privilege of learning reliable models based on
already-defined contextual features, online training for streaming data may be
more challenging -- the data is streamed through time, and the underlying
context during a data generation process may change. Furthermore, the problem
is exacerbated when the number of possible context is not known. In this study,
we propose an online-learning algorithm involving the use of a neural
network-based autoencoder to identify contextual changes during training, then
compares the currently-inferred context to a knowledge base of learned contexts
as training advances. Results show that classifier-training benefits from the
automatically discovered contexts which demonstrates quicker learning
convergence during contextual changes compared to current methods.
