文章基本信息

标题：An overview on data representation learning: From traditional feature learning to recent deep learning
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作者：Guoqiang Zhong ; Guoqiang Zhong ; Li-Na Wang 等
期刊名称：The Journal of Finance and Data Science
印刷版ISSN：2405-9188
出版年度：2016
卷号：2
期号：4
页码：265-278
DOI：10.1016/j.jfds.2017.05.001
语种：English
出版社：Elsevier
摘要：Abstract Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, either linear or nonlinear, either supervised or unsupervised, either “shallow” or “deep”. 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 online resources (e.g., courses, tutorials and books) and toolboxes are provided. At the end, we give a few remarks on the development of data representation learning and suggest some interesting research directions in this area.
关键词：Representation learning;Feature learning;Deep learning