Name
Title | ||
|---|---|---|
An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning. |
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2016 | The Journal of Finance and Data Science | Robot learning,Algorithmic learning theory,Semi-supervised learning,Multi-task learning,Instance-based learning,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Machine learning,Feature learning |
DocType | Volume | Citations |
Journal | abs/1611.08331 | 2 |
PageRank | References | Authors |
0.36 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guoqiang Zhong | 1 | 123 | 20.68 |
| Li-Na Wang | 2 | 2 | 1.71 |
| Junyu Dong | 3 | 393 | 77.68 |