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 MA Shilong,WUNIRI Qiqige,LI Xiaoping.Deep learning with big data: state of the art and development[J].CAAI Transactions on Intelligent Systems,2016,11(6):728-742.[doi:10.11992/tis.201611021]





Deep learning with big data: state of the art and development
马世龙 乌尼日其其格 李小平
北京航空航天大学 软件开发环境国家重点实验室, 北京 100191
MA Shilong WUNIRI Qiqige LI Xiaoping
State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
big datamachine learningdeep networkdeep learningneural networkartificial intelligencelearning algorithmderivation tree
As the era of the big data arrives, it is accompanied by profound changes to traditional data science based on statistics. Big data also pushes innovations in the methods of data analysis. Deep learning that evolves from machine learning and multilayer neural networks are currently extremely active research areas. From the symbolic machine learning and statistical machine learning to the artificial neural network, followed by data mining in the 90s, this has built a solid foundation for deep learning (DL) that makes it a notable tool for discovering the potential value behind big data. This survey compactly summarized big data and DL, proposed a generative relationship tree of the major deep networks and the algorithms, illustrated a broad area of applications based on DL, and highlighted the challenges to DL with big data, as well as identified future trends.


[1] TOLLE K M, TANSLEY D, HEY A J G. The fourth paradigm:data-intensive scientific discovery[J]. Proceedings of the IEEE, 2012, 99(8):1334-7.
[2] MASHEY J R. Big Data and the next wave of infra stress[D] Berkeley:University of California, 1997.
[3] MAYER-SCHÖNBERGER V, CUKIER K. A big data:a revolution that will transform how we live, work, and think[M]. Boston:Eamon Dolan, 2013.
[4] HILBERT M, LÓPEZ P. The world’s technological capacity to store, communicate, and compute information[J]. Science, 2011, 332(6025):60-65.
[5] LANEY D. 3D data management:controlling data volume, velocity, and variety[R]. META Group Research Note, 2001.
[6] IDC. IIIS:the "four vs" of big data[EB/OL].[2016-11-11]. http://www.computerworld.com.au/article/396198/iiis_four_vs_big_data/.
[7] SCHROECK M J, SHOCKLEY R, SMART J, et al. Analytics:the real-world use of big data[R]. Oxford:IBM, 2012.
[8] IBM. The four v’s of big data[EB/OL]. 2014[2016-11-11]. http://www.ibmbigdatahub.com/infographic/four-vs-big-data.
[9] 郭平, 王可, 罗阿理, 等. 大数据分析中的计算智能研究现状与展望[J]. 软件学报, 2015, 26(11):3010-3025. GUO Ping, WANG Ke, LUO Ali, et al. Computational intelligence for big data analysis:current status and future prospect[J]. Journal of software, 2015, 26(11):3010-3025.
[10] Gartner. Big data[EB/OL].[2016-11-11]. http://www.gartner.com/it-glossary/big-data/.
[11] MANYIKA J, CHUI M, BROWN B, et al. Big data:the next frontier for innovation, competition, and productivity[R]. Analytics:McKinsey & Company, 2011.
[12] Wikiprdia. Big data[EB/OL]. 2009.[2016-11-11]. https://en.wikipedia.org/wiki/Big_data.
[13] JAMES J. How much data is created every minute?[EB/OL].[2016-11-11]. https://www.domo.com/blog/how-much-data-is-created-every-minute/.
[14] 维克托·迈尔·舍恩伯格, 周涛. 大数据时代生活、工作与思维的大变革[M]. 周涛, 译. 杭州:浙江人民出版社, 2013:136-136.
[15] 孟小峰,慈祥. 大数据管理:概念、技术与挑战[J]. 计算机研究与发展, 2013, 50(1):146-169. MENG Xiaofeng, CI Xiang. Big data management:concepts, techniques and challenges[J]. Journal of computer research and development, 2013, 50(1):146-169.
[16] 王意洁, 孙伟东, 周松, 等. 云计算环境下的分布存储关键技术[J]. 软件学报, 2012, 23(4):962-986. WANG Yijie, SUN Weidong, ZHOU Song, et al. Key technologies of distributed storage for cloud computing[J]. Journal of software, 2012, 23(4):962-986.
[17] GANTZ J, REINSEL D. Extracting value from chaos[R]. Idcemc2 Report, 2011.
[18] 程学旗, 靳小龙, 王元卓, 等. 大数据系统和分析技术综述[J]. 软件学报, 2014, 25(9):1889-1908. CHENG Xueqi, JIN Xiaolong, WANG Yuanzhuo, et al. Survey on big data system and analytic technology[J]. Journal of software, 2014, 25(9):1889-1908.
[19] STUART D. The data revolution:big data, open data, data infrastructures and their consequences[J]. Online information review, 2015, 39(2):272.
[20] LABRINIDIS A, JAGADISH H V. Challenges and opportunities with big data[J]. Proceedings of the vldb endowment, 2012, 5(12):2032-2033.
[21] ABU-MOSTAFA Y S, MAGDON-ISMAIL M, LIN H T. Learning from data:a short course[M]. Chicago:Amlbook, 2012.
[22] 洪家荣. 机器学习--回顾与展望[J]. 计算机科学, 1991, 18(2):1-8. HONG Jiarong. Machine learning-review and vision[J]. Computer science, 1991,18(2):1-8.
[23] SAMUEL A L. Some studies in machine learning using the game of checkers. II-recent progress[J]. Annual review in automatic programming, 1969, 6:1-36.
[24] ROSENBLATT F. The perceptron-a perceiving and recognizing automaton[R]. Ithaca, NY:Cornell Aeronautical Laboratory, 1957.
[25] WIDROW B, LEHR M A. 30 years of adaptive neural networks:perceptron, Madaline, and backpropagation[J]. Proceedings of the IEEE, 1990, 78(9):1415-1442.
[26] MINSKY M, PAPERT S A. Perceptrons:an introduction to computational geometry, expanded edition[M]. Cambridge, Mass:MIT Press, 1988:449-452.
[27] 王珏, 石纯一. 机器学习研究[J]. 广西师范大学学报:自然科学版, 2003, 21(2):1-15. WANG Jue, SHI Chunyi. Investigations on machine learning[J]. Journal of Guangxi normal university:natural science edition, 2003, 21(2):1-15.
[28] CORTES C, VAPNIK V. Support-vector networks[J]. Machine learning, 1995, 20(3):273-297.
[29] REYNOLDS D A, ROSE R C, SMITH M J T. A mixture modeling approach to text-independent speaker identification[J]. Journal of the acoustical society of america, 1990, 87(S1):109.
[30] RUMELHART D E, MCCLELLAND J L. Parallel distributed processing:explorations in the microstructure of cognition:foundations[M]. Cambridge, Mass:MIT Press, 1987.
[31] WERBOS P J. Backpropagation through time:what it does and how to do it[J]. Proceedings of the IEEE, 1990, 78(10):1550-1560.
[32] WU Xindong, KUMAR V, QUINLAN J R, et al. Top 10 algorithms in data mining[J]. Knowledge and information systems, 2008, 14(1):1-37.
[33] GORI M, TESI A. on the problem of local minima in backpropagation[J]. IEEE transactions on pattern analysis and machine intelligence, 1992, 14(1):76-86.
[34] FLETCHER L, KATKOVNIK V, STEFFENS F E, et al. Optimizing the number of hidden nodes of a feedforward artificial neural network[C]//Proceedings of 1998 IEEE International Joint Conference Neural Networks. Anchorage, AK:IEEE, 1998, 2:1608-1612.
[35] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553):436-444.
[36] BENGIO Y, COURVILLE A, VINCENT P. A courville and P vincent, representation learning:a review and new perspectives[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(8):1798-1828.
[37] ACKLEY D H, HINTON G E, SEJNOWSKI T J. A learning algorithm for boltzmann machines[J]. Cognitive science, 1985, 9(1):147-169.
[38] 刘建伟, 刘媛, 罗雄麟. 玻尔兹曼机研究进展[J]. 计算机研究与发展, 2014, 51(1):1-16. LIU Jianwei, LIU Yuan, LUO Xionglin. Research and development on boltzmann machine[J]. Journal of computer research and development, 2014, 51(1):1-16.
[39] SALAKHUTDINOV R, HINTON G. Deep boltzmann machines[J]. Journal of machine learning research, 2009, 5(2):1997-2006.
[40] SMOLENSKY P. Information processing in dynamical systems:foundations of harmony theory[M]. Cambridge, Mass:MIT Press, 1986:194-281.
[41] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[42] HINTON G E. Training products of experts by minimizing contrastive divergence[J]. Neural computation, 2002, 14(8):1771-800.
[43] 张建明, 詹智财, 成科扬, 等. 深度学习的研究与发展[J]. 江苏大学学报:自然科学版, 2015, 36(2):191-200. ZHANG Jianming, ZHAN Zhicai, CHENG Keyang, et al. Review on development of deep learning[J]. Journal of Jiangsu university:natural science edition, 2015, 36(2):191-200.
[44] 孙志远, 鲁成祥, 史忠植, 等. 深度学习研究与进展[J]. 计算机科学, 2016, 43(2):1-8. SUN Zhiyuan, LU Chengxiang, SHI Zhongzhi, et al. Research and advances on deep learning[J]. Computer science, 2016, 43(2):1-8.
[45] SCHMIDHUBER J. Deep learning in neural networks:an overview[J]. Neural networks, 2015,
[46] CHEN H, MURRAY A. A continuous restricted boltzmann machine with a hardware-amenable learning algorithm[J]. Lecture notes in computer science, 2002, 2415:358-363.
[47] LUO Heng, SHEN Ruimin, NIU Changyong. Sparse group restricted boltzmann machines[C]//Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. San Francisco, California, Usa:AAAI Press, 2010.
[48] LEE H, LARGMAN Y, PHAM P, et al. Unsupervised feature learning for audio classification using convolutional deep belief networks[C]//Advances in Neural Information Processing Systems 22:Conference on Neural Information Processing Systems 2009. Vancouver, British Columbia, Canada, 2009.
[49] HALKIAS X, PARIS S, GLOTIN H. Sparse penalty in deep belief networks:using the mixed norm constraint[J]. Computer science, 2013.
[50] POUGETABADIE J,MIRZA M,XU Bing,et al. Generative adversarial nets[J]. Advances in neural information processing systems,2014,3:2672-2680.
[51] YOSHUA Bengio, PASCAL Lamblin, DAN Popovici, et al. Greedy layer-wise training of deep networks[C]//Proceedings of the Nips, Canada, 2006:153-160.
[52] RANZATO M A, BOUREAU Y L, LECUN Y. Sparse feature learning for deep belief networks[J]. Advances in neural information processing systems, 2007, 20:1185-1192.
[53] VINCENT P, LAROCHELLE H, BENGIO Y, et al. Extracting and composing robust features with denoising autoencoders[C]//Proceedings of the International Conference, F, 2008.
[54] VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked denoising autoencoders:learning useful representations in a deep network with a local denoising criterion[J]. Journal of machine learning research, 2010, 11(12):3371-408.
[55] JIANG Xiaojuan, ZHANG Yinghua, ZHANG Wensheng, et al. A novel sparse auto-encoder for deep unsupervised learning[C]//Proceedings of Sixth International Conference on Advanced Computational Intelligence. Hangzhou:IEEE, 2013:256-261.
[56] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-324.
[57] WANG Wei, OOI B C, YANG Xiaoyan, et al. Effective multi-modal retrieval based on stacked auto-encoders[J]. Proceedings of the VLDB endowment, 2014, 7(8):649-660.
[58] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 2012, 25(2):2012.
[59] ELMAN J L. Finding structure in time[J]. Cognitive science, 1990, 14(2):179-211.
[60] HIHI S E, HC-J M Q, BENGIO Y. Hierarchical recurrent neural networks for long-term dependencies[J]. Advances in neural information processing systems, 1995, 8(493-9.
[61] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural computation, 1997, 9(8):1735-1780.
[62] CHO K, MERRIENBOER B V, BAHDANAU D, et al. On the properties of neural machine translation:encoder-decoder approaches[J]. Computer science, 2014,
[63] MNIH V, HEESS N, GRAVES A, et al. Recurrent models of visual attention[J]. Computer science, 2014, 3(2204-12.
[64] GOODFELLOW I, POUGETABADIE J, MIRZA M, et al. Generative adversarial Nets[J]. Advances in neural information processing systems, 2014, 2672-80.
[65] RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. Computer science, 2015,
[66] XUE J H, TITTERINGTON D M. Comment on "on discriminative vs. generative classifiers:a comparison of logistic regression and naive Bayes"[J]. Neural processing letters, 2008, 2(3):169-87.
[67] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7):1527-1554.
[68] LECUN Y, JACKEL L D, BOTTOU L, et al. Learning algorithms for classification:a comparison on handwritten digit recognition[M]//OH J H, CHO S. Neural Networks:The Statistical Mechanics Perspective. Singapore:World Scientific, 1995.
[69] LE Q V. Building high-level features using large scale unsupervised learning[C]//Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Vancouver, BC:IEEE, 2013:8595-8598.
[70] NYTIMES. In a big network of computers evidence of machine learning[EB/OL].[2016-11-11].http://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html?pagewanted=all.
[71] SUN Yi, WANG Xiaogang, TANG Xiaoou. Deep learning face representation from predicting 10,000 classes[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH:IEEE, 2014.
[72] BBC. Artificial intelligence:Google’s AlphaGo beats Go master lee Se-dol[EB/OL]. 2016.[2016-11-11]. http://www.bbc.com/news/technology-35785875.
[73] SILVER D, HUANG J, MADDISON C J, et al. Mastering the game of go with deep neural networks and tree search[J]. Nature, 2016, 529(7587):484-489.
[74] MOHAMED A R, DAHL G E, HINTON G. Acoustic modeling using deep belief networks[J]. IEEE transactions on audio, speech, and language processing, 2012, 20(1):14-22.
[75] PAN Jia, LIU Cong, WANG Zhiguo, et al. Investigation of deep neural networks (DNN) for large vocabulary continuous speech recognition:why DNN surpasses GMMS in acoustic modeling[C]//Proceedings of the 8th International Symposium on Chinese Spoken Language Processing (ISCSLP). Kowloon:IEEE, 2012:301-305.
[76] Microsoft. Microsoft audio video indexing service[EB/OL].[2016-11-11]. https://www.microsoft.com/en-us/research/project/mavis/.
[77] SEIDE F, LI Gang, YU Dong. Conversational speech transcription using context-dependent deep neural networks[C]//INTERSPEECH 2011, Conference of the International Speech Communication Association. Florence, Italy, 2011.
[78] MORIN F, BENGIO Y. Hierarchical probabilistic neural network language model[C]//Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics. Society for Artificial Intelligence and Statistics, 2005.
[79] COLLOBERT R, WESTON J. A unified architecture for natural language processing:deep neural networks with multitask learning[C]//Proceedings of the 25th International Conference on Machine Learning (ICML). NEC Laboratories America, Inc, 2008.
[80] MNIH A, HINTON G. A scalable hierarchical distributed language model[C]. Proceedings of the Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2008.
[81] MIKOLOV T, KOMBRINK S, ?ERNOCKI J, et al. Extensions of recurrent neural network language model[C]//Proceedings of 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague:IEEE, 2011.
[82] MIKOLOV T, DEORAS A, POVEY D, et al. Strategies for training large scale neural network language models[C]//Proceedings of 2011 IEEE Workshop on Automatic Speech Recognition and Understanding. Waikoloa, HI:IEEE, 2011.
[83] MIKOLOV T, ZWEIG G. Context dependent recurrent neural network language model[C]//Proceedings of 2012 IEEE Spoken Language Technology Workshop (SLT). Miami, FL:IEEE, 2012.
[84] MIKOLOV T, KARAFIÁT M, BURGET L, et al. Recurrent neural network based language model[C]//Proceedings of the INTERSPEECH 2010, 11th Conference of the International Speech Communication Association. Makuhari, Chiba, Japan, 2010.
[85] HUANG E H, SOCHER R, MANNING C D, et al. Improving word representations via global context and multiple word prototypes[C]//Proceedings of the Meeting of the Association for Computational Linguistics:Long Papers, F, 2012.
[86] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[J]. Computer science, 2013,
[87] BAEZA-YATES R A, RIBEIRO-NETO B. Modern information retrieval:the concepts and technology behind search[M]. 2nd ed. New York:Addison Wesley,2011:26-28.
[89] HARRINGTON P. Machine learning in action[M]. Shelter Island, N.Y.:Manning Publications Co, 2012.
[90] 郑胤, 陈权崎, 章毓晋. 深度学习及其在目标和行为识别中的新进展[J]. 中国图象图形学报, 2014, 19(2):175-184. ZHENG Yin, CHEN Quanqi, ZHANG Yujin. Deep learning and its new progress in object and behavior recognition[J]. Journal of image and graphics, 2014, 19(2):175-184.
[91] CHEN Xuewen, LIN Xiaotong. Big data deep learning:challenges and perspectives[J]. IEEE access, 2014, 2:514-525.


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