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

An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning