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论文类型：期刊论文

发表时间：2018-07-01

发表刊物：INFORMATION FUSION

收录刊物：SCIE、EI、ESI高被引论文、ESI热点论文

卷号：42

页面范围：146-157

ISSN号：1566-2535

关键字：Deep learning; Big data; Stacked auto-encoders; Deep belief networks; Convolutional neural networks; Recurrent neural networks

摘要：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.