  Since about 100 years ago, to learn the intrinsic structure of data, many
representation learning approaches have been proposed, including both linear
ones and nonlinear ones, supervised ones and unsupervised ones. Particularly,
deep architectures are widely applied for representation learning in recent
years, and have delivered top results in many tasks, such as image
classification, object detection and speech recognition. In this paper, we
review the development of data representation learning methods. Specifically,
we investigate both traditional feature learning algorithms and
state-of-the-art deep learning models. The history of data representation
learning is introduced, while available resources (e.g. online course, tutorial
and book information) and toolboxes are provided. Finally, we conclude this
paper with remarks and some interesting research directions on data
representation learning.
