  The current practice of manually processing features for high-dimensional and
heterogeneous aviation data is labor-intensive, does not scale well to new
problems, and is prone to information loss, affecting the effectiveness and
maintainability of machine learning (ML) procedures. This research explored an
unsupervised learning method, autoencoder, to extract effective features for
aviation machine learning problems. The study explored variants of autoencoders
with the aim of forcing the learned representations of the input to assume
useful properties. A flight track anomaly detection autoencoder was developed
to demonstrate the versatility of the technique. The research results show that
the autoencoder can not only automatically extract effective features for the
flight track data, but also efficiently deep clean data, thereby reducing the
workload of data scientists. Moreover, the research leveraged transfer learning
to efficiently train models for multiple airports. Transfer learning can reduce
model training times from days to hours, as well as improving model
performance. The developed applications and techniques are shared with the
whole aviation community to improve effectiveness of ongoing and future machine
learning studies.
