A survey on deep learning for big data

被引:724
作者
Zhang, Qingchen [1 ,2 ]
Yang, Laurence T. [1 ,2 ]
Chen, Zhikui [3 ]
Li, Peng [3 ]
机构
[1] Univ Elect Sci & Technol China, Sch Elect Engn, Chengdu, Sichuan, Peoples R China
[2] St Francis Xavier Univ, Dept Comp Sci, Antigonish, NS, Canada
[3] Dalian Univ Technol, Sch Software Technol, Dalian, Peoples R China
关键词
Deep learning; Big data; Stacked auto-encoders; Deep belief networks; Convolutional neural networks; Recurrent neural networks; CONVOLUTIONAL NEURAL-NETWORKS; BELIEF NETWORKS; CLASSIFICATION; CHALLENGES; PREDICTION; STACKING;
D O I
10.1016/j.inffus.2017.10.006
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页码:146 / 157
页数:12
相关论文
共 102 条
  • [21] [Anonymous], SPEEDING UP CONVOLUT
  • [22] [Anonymous], 2017, ARXIV170309039
  • [23] [Anonymous], 2009, ICML
  • [24] Becker D, 2015, PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON BIG DATA, P2644, DOI 10.1109/BigData.2015.7364064
  • [25] Social big data: Recent achievements and new challenges
    Bello-Orgaz, Gema
    Jung, Jason J.
    Camacho, David
    [J]. INFORMATION FUSION, 2016, 28 : 45 - 59
  • [26] Representation Learning: A Review and New Perspectives
    Bengio, Yoshua
    Courville, Aaron
    Vincent, Pascal
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2013, 35 (08) : 1798 - 1828
  • [27] The Meaningful Use of Big Data: Four Perspectives - Four Challenges
    Bizer, Christian
    Boncz, Peter
    Brodie, Michael L.
    Erling, Orri
    [J]. SIGMOD RECORD, 2011, 40 (04) : 56 - 60
  • [28] [卜范玉 Bu Fanyu], 2015, [计算机工程与应用, Computer Engineering and Application], V51, P21
  • [29] [卜范玉 Bu Fanyu], 2014, [微电子学与计算机, Microelectronics & Computer], V31, P173
  • [30] EXPLAINING THE GIBBS SAMPLER
    CASELLA, G
    GEORGE, EI
    [J]. AMERICAN STATISTICIAN, 1992, 46 (03) : 167 - 174