In machine learning, feature learning or representation learning is a
set of techniques that allows a system to automatically discover the
representations needed for feature detection or classification from raw data.
This replaces manual feature engineering and allows a machine to both learn
the features and use them to perform a specific task.
Feature learning is motivated by the fact that machine learning tasks such as
classification often require input that is mathematically and computationally
convenient to process. However, real-world data such as images, video, and
sensor data has not yielded to attempts to algorithmically define specific
features.