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Indexed by:期刊论文

Date of Publication:2018-07-01

Journal:INFORMATION FUSION

Included Journals:SCIE、EI、ESI高被引论文、ESI热点论文

Volume:42

Page Number:146-157

ISSN No.:1566-2535

Key Words:Deep learning; Big data; Stacked auto-encoders; Deep belief networks; Convolutional neural networks; Recurrent neural networks

Abstract:Deep learning, as one of the most currently remarkable machine learning techniques, has achieved great success in many applications such as image analysis, speech recognition and text understanding. It uses supervised and unsupervised strategies to learn multi-level representations and features in hierarchical architectures for the tasks of classification and pattern recognition. Recent development in sensor networks and communication technologies has enabled the collection of big data. Although big data provides great opportunities for a broad of areas including e-commerce, industrial control and smart medical, it poses many challenging issues on data mining and information processing due to its characteristics of large volume, large variety, large velocity and large veracity. In the past few years, deep learning has played an important role in big data analytic solutions. In this paper, we review the emerging researches of deep learning models for big data feature learning. Furthermore, we point out the remaining challenges of big data deep learning and discuss the future topics.