# An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning Source: [An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:31:19.733Z Authors: Guoqiang Zhong, Li-Na Wang, Junyu Dong Published: 2016-11-25T00:53:37.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()