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Volumn 42, Issue , 2018, Pages 146-157

A survey on deep learning for big data

Author keywords

Big data; Convolutional neural networks; Deep belief networks; Deep learning; Recurrent neural networks; Stacked auto encoders

Indexed keywords

CHARACTER RECOGNITION; DATA MINING; DEEP LEARNING; LEARNING SYSTEMS; MEDICAL COMPUTING; NEURAL NETWORKS; PATTERN RECOGNITION; RECURRENT NEURAL NETWORKS; SENSOR NETWORKS; SPEECH RECOGNITION;

EID: 85033462391     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.inffus.2017.10.006     Document Type: Short Survey
Times cited : (960)

References (102)
  • 1
    • 85086756939 scopus 로고    scopus 로고
    • An integration framework on cloud for cyber physical social systems big data
    • 10.1109/TCC.2015.2511766
    • Kuang, L., Yang, L.T., Liao, Y., An integration framework on cloud for cyber physical social systems big data. IEEE Trans. Cloud Comput., 2015 10.1109/TCC.2015.2511766.
    • (2015) IEEE Trans. Cloud Comput.
    • Kuang, L.1    Yang, L.T.2    Liao, Y.3
  • 2
    • 84881061591 scopus 로고    scopus 로고
    • Communication energy modeling and optimization through joint packet size analysis of BSN and wifi networks
    • Li, Y., Qi, X., Keally, M., Ren, Z., Zhou, G., Xiao, D., Deng, S., Communication energy modeling and optimization through joint packet size analysis of BSN and wifi networks. IEEE Trans. Parallel Distrib. Syst. 24:9 (2013), 1741–1751.
    • (2013) IEEE Trans. Parallel Distrib. Syst. , vol.24 , Issue.9 , pp. 1741-1751
    • Li, Y.1    Qi, X.2    Keally, M.3    Ren, Z.4    Zhou, G.5    Xiao, D.6    Deng, S.7
  • 3
    • 84900800509 scopus 로고    scopus 로고
    • Data-intensive applications, challenges, techniques and technologies: a survey on big data
    • 2014
    • Chen, C.L.P., Zhang, C., Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf. Sci. 275 (2014), 314–347 2014.
    • (2014) Inf. Sci. , vol.275 , pp. 314-347
    • Chen, C.L.P.1    Zhang, C.2
  • 5
    • 84943350772 scopus 로고    scopus 로고
    • Social big data: recent achievements and new challenges
    • Orgaz, G.B., Jung, J.J., Camacho, D., Social big data: recent achievements and new challenges. Inf. Fusion 28 (2016), 45–59.
    • (2016) Inf. Fusion , vol.28 , pp. 45-59
    • Orgaz, G.B.1    Jung, J.J.2    Camacho, D.3
  • 6
    • 84939242660 scopus 로고    scopus 로고
    • A framework for composition and enforcement of privacy-aware and context-driven authorization mechanism for multimedia big data
    • Samuel, A., Sarfraz, M.I., Haseeb, H., Basalamah, S., Ghafoor, A., A framework for composition and enforcement of privacy-aware and context-driven authorization mechanism for multimedia big data. IEEE Trans. Multimedia 17:9 (2015), 1484–1494.
    • (2015) IEEE Trans. Multimedia , vol.17 , Issue.9 , pp. 1484-1494
    • Samuel, A.1    Sarfraz, M.I.2    Haseeb, H.3    Basalamah, S.4    Ghafoor, A.5
  • 8
    • 84923318381 scopus 로고    scopus 로고
    • Big data deep learning: challenges and perspectives
    • Chen, X., Lin, X., Big data deep learning: challenges and perspectives. IEEE Access 2 (2014), 514–525.
    • (2014) IEEE Access , vol.2 , pp. 514-525
    • Chen, X.1    Lin, X.2
  • 10
    • 84930630277 scopus 로고    scopus 로고
    • Deep learning, Nature 521, 7553
    • Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521(7553) 436–444.
    • LeCun, Y.1    Bengio, Y.2    Hinton, G.3
  • 11
    • 84910651844 scopus 로고    scopus 로고
    • Deep learning in neural networks: an overview
    • Schmidhuber, J., Deep learning in neural networks: an overview. Neural Netw. 61 (2015), 85–117.
    • (2015) Neural Netw. , vol.61 , pp. 85-117
    • Schmidhuber, J.1
  • 12
    • 33746600649 scopus 로고    scopus 로고
    • Reducing the dimensionality of data with neural networks
    • Hinton, G.E., Salakhutdinov, R.R., Reducing the dimensionality of data with neural networks. Science 313:5786 (2006), 504–507.
    • (2006) Science , vol.313 , Issue.5786 , pp. 504-507
    • Hinton, G.E.1    Salakhutdinov, R.R.2
  • 14
    • 85033443272 scopus 로고    scopus 로고
    • Efficient processing of deep neual networks: a tutoria and survey
    • V. Sze, Y. Chen, T. Yang, J. Emer, Efficient processing of deep neual networks: a tutoria and survey, 2017, arXiv:1703.09039.
    • (2017)
    • Sze, V.1    Chen, Y.2    Yang, T.3    Emer, J.4
  • 18
    • 84929327770 scopus 로고    scopus 로고
    • Multitask learning of deep neural networks for low-resource speech recognition
    • Chen, D., Mak, B.K., Multitask learning of deep neural networks for low-resource speech recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 23:7 (2015), 1172–1183.
    • (2015) IEEE/ACM Trans. Audio Speech Lang. Process. , vol.23 , Issue.7 , pp. 1172-1183
    • Chen, D.1    Mak, B.K.2
  • 19
    • 85017176110 scopus 로고    scopus 로고
    • Deep learning-based document modeling for personality detection from text
    • Majumder, N., Poria, S., Gelbukh, A., Cambria, E., Deep learning-based document modeling for personality detection from text. IEEE Intell. Syst. 32:2 (2017), 74–79.
    • (2017) IEEE Intell. Syst. , vol.32 , Issue.2 , pp. 74-79
    • Majumder, N.1    Poria, S.2    Gelbukh, A.3    Cambria, E.4
  • 22
    • 84971373627 scopus 로고    scopus 로고
    • Mobile big data analytics using deep learning and apache spark
    • Alsheikh, M.A., Niyato, D., Lin, S., Tan, H., Han, Z., Mobile big data analytics using deep learning and apache spark. IEEE Netw. 30:3 (2016), 22–29.
    • (2016) IEEE Netw. , vol.30 , Issue.3 , pp. 22-29
    • Alsheikh, M.A.1    Niyato, D.2    Lin, S.3    Tan, H.4    Han, Z.5
  • 25
    • 84988876898 scopus 로고    scopus 로고
    • Learning cascaded deep auto-encoder networks for face alignment
    • Weng, R., Lu, J., Tan, Y., Zhou, J., Learning cascaded deep auto-encoder networks for face alignment. IEEE Trans. Multimedia 18:10 (2016), 2066–2078.
    • (2016) IEEE Trans. Multimedia , vol.18 , Issue.10 , pp. 2066-2078
    • Weng, R.1    Lu, J.2    Tan, Y.3    Zhou, J.4
  • 26
    • 84957055585 scopus 로고    scopus 로고
    • Unseen noise estimation using separable deep auto encoder for speech enhancement
    • Sun, M., Zhang, X., Hmme, H.V., Zheng, T.F., Unseen noise estimation using separable deep auto encoder for speech enhancement. IEEE/ACM Trans. Audio Speech Lang. Process. 24:1 (2016), 93–104.
    • (2016) IEEE/ACM Trans. Audio Speech Lang. Process. , vol.24 , Issue.1 , pp. 93-104
    • Sun, M.1    Zhang, X.2    Hmme, H.V.3    Zheng, T.F.4
  • 28
    • 85033471385 scopus 로고    scopus 로고
    • Sparse autoencoder, CS294A Lecture notes 72
    • A. Ng, Sparse autoencoder, CS294A Lecture notes 72(2011) 1–19.
    • (2011) , pp. 1-19
    • Ng, A.1
  • 29
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets, Neural Comput. 18(7)
    • G.E. Hinton, S. Osindero, Y.-W. Teh, A fast learning algorithm for deep belief nets, Neural Comput. 18(7) 1527–1554.
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.-W.3
  • 30
    • 84859473821 scopus 로고    scopus 로고
    • Learning algorithms for the classification restricted boltzmann machine
    • Larochelle, H., Mandel, M., Pascanu, R., Bengio, Y., Learning algorithms for the classification restricted boltzmann machine. J. Mach. Learn. Res. 13 (2012), 643–669.
    • (2012) J. Mach. Learn. Res. , vol.13 , pp. 643-669
    • Larochelle, H.1    Mandel, M.2    Pascanu, R.3    Bengio, Y.4
  • 36
    • 84872300403 scopus 로고    scopus 로고
    • Deep belief networks based voice activity detection
    • Zhang, X., Wu, J., Deep belief networks based voice activity detection. IEEE Trans. Audio Speech Lang. Process. 21:4 (2013), 697–710.
    • (2013) IEEE Trans. Audio Speech Lang. Process. , vol.21 , Issue.4 , pp. 697-710
    • Zhang, X.1    Wu, J.2
  • 37
    • 84907500988 scopus 로고    scopus 로고
    • Deep architecture for traffic flow prediction: deep belief networks with multitask learning
    • Huang, W., Song, G., Hong, H., Xie, K., Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. 15:5 (2014), 2191–2201.
    • (2014) IEEE Trans. Intell. Transp. Syst. , vol.15 , Issue.5 , pp. 2191-2201
    • Huang, W.1    Song, G.2    Hong, H.3    Xie, K.4
  • 40
    • 84992121956 scopus 로고    scopus 로고
    • Convolutional neural networks for large-scale remote-sensing image classification
    • Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P., Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans. Geosci. Remote Sens. 55:2 (2017), 645–657.
    • (2017) IEEE Trans. Geosci. Remote Sens. , vol.55 , Issue.2 , pp. 645-657
    • Maggiori, E.1    Tarabalka, Y.2    Charpiat, G.3    Alliez, P.4
  • 41
    • 85007475072 scopus 로고    scopus 로고
    • Deep convolutional neural networks for predominant instrument recognition in polyphonic music
    • Han, Y., Kim, J., Lee, K., Deep convolutional neural networks for predominant instrument recognition in polyphonic music. IEEE/ACM Trans. Audio Speech Lang. Process. 25:1 (2017), 208–221.
    • (2017) IEEE/ACM Trans. Audio Speech Lang. Process. , vol.25 , Issue.1 , pp. 208-221
    • Han, Y.1    Kim, J.2    Lee, K.3
  • 43
    • 85033444449 scopus 로고    scopus 로고
    • Very deep convolutional networks for large-scale image recognition
    • K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014, arXiv:1409.1556.
    • (2014)
    • Simonyan, K.1    Zisserman, A.2
  • 44
    • 85033434077 scopus 로고    scopus 로고
    • Deep learning and its applications to machine health monitoring: a survey
    • R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, R.X. Gao, Deep learning and its applications to machine health monitoring: a survey, 2016, arXiv:1612.07640.
    • (2016)
    • Zhao, R.1    Yan, R.2    Chen, Z.3    Mao, K.4    Wang, P.5    Gao, R.X.6
  • 45
    • 85033447461 scopus 로고    scopus 로고
    • A convolutional neural network for modeling sentences
    • N. Kalchbrenner, E. Grefenstette, P. Blunsom, A convolutional neural network for modeling sentences, 2014, arXiv:1404.2188.
    • (2014)
    • Kalchbrenner, N.1    Grefenstette, E.2    Blunsom, P.3
  • 48
    • 84991384259 scopus 로고    scopus 로고
    • Very deep convolutional neural networks for noise robust speech recognition
    • Qian, Y., Bi, M., Tan, T., Yu, K., Very deep convolutional neural networks for noise robust speech recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 24:12 (2016), 2263–2276.
    • (2016) IEEE/ACM Trans. Audio Speech Lang. Process. , vol.24 , Issue.12 , pp. 2263-2276
    • Qian, Y.1    Bi, M.2    Tan, T.3    Yu, K.4
  • 50
    • 84901999583 scopus 로고    scopus 로고
    • Convolutional neural networks for distant speech recognition
    • Swietojanski, P., Ghoshal, A., Renals, S., Convolutional neural networks for distant speech recognition. IEEE Signal Process. Lett. 21:9 (2014), 1120–1124.
    • (2014) IEEE Signal Process. Lett. , vol.21 , Issue.9 , pp. 1120-1124
    • Swietojanski, P.1    Ghoshal, A.2    Renals, S.3
  • 51
  • 52
    • 0034293152 scopus 로고    scopus 로고
    • Learning to torget: continual prediction with istm
    • Gers, F.A., Schmidhuber, J., Cummins, F., Learning to torget: continual prediction with istm. Neural Comput. 12:10 (2000), 2451–2471.
    • (2000) Neural Comput. , vol.12 , Issue.10 , pp. 2451-2471
    • Gers, F.A.1    Schmidhuber, J.2    Cummins, F.3
  • 54
    • 85033473140 scopus 로고    scopus 로고
    • Empirical evaluation of gated recurrent neural networks on sequence modeling, 1412.3555.
    • J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, 2014, 1412.3555.
    • (2014)
    • Chung, J.1    Gulcehre, C.2    Cho, K.3    Bengio, Y.4
  • 58
    • 85008430487 scopus 로고    scopus 로고
    • End-to-end online writer identification with recurrent neural network
    • Zhang, X., Xie, G., Liu, C., Bengio, Y., End-to-end online writer identification with recurrent neural network. IEEE Trans. Hum. Mach. Syst. 47:2 (2017), 285–292.
    • (2017) IEEE Trans. Hum. Mach. Syst. , vol.47 , Issue.2 , pp. 285-292
    • Zhang, X.1    Xie, G.2    Liu, C.3    Bengio, Y.4
  • 59
    • 84949921270 scopus 로고    scopus 로고
    • Bayesian recurrent neural network for language modeling
    • Chien, J., Ku, Y., Bayesian recurrent neural network for language modeling. IEEE Trans. Neural Netw. Learn. Syst. 27:2 (2016), 361–374.
    • (2016) IEEE Trans. Neural Netw. Learn. Syst. , vol.27 , Issue.2 , pp. 361-374
    • Chien, J.1    Ku, Y.2
  • 66
    • 0021518209 scopus 로고
    • Stochastic relaxation, gibbs distributions, and the bayesian restoration of images
    • Geman, S., Geman, D., Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6:6 (1984), 721–741.
    • (1984) IEEE Trans. Pattern Anal. Mach. Intell. , vol.6 , Issue.6 , pp. 721-741
    • Geman, S.1    Geman, D.2
  • 67
    • 84937730674 scopus 로고
    • Explaining the gibbs sampler
    • Casella, G., George, E., Explaining the gibbs sampler. Am. Stat. 46:3 (1992), 167–174.
    • (1992) Am. Stat. , vol.46 , Issue.3 , pp. 167-174
    • Casella, G.1    George, E.2
  • 76
    • 85033454118 scopus 로고    scopus 로고
    • Speeding-up convolutional neural networks ssing fine-tuned CP-decomposition, 1412.6553.
    • V. Lebedev, Y. Ganin, M. Rakhuba, I. Oseledets, V. Lempitsky, Speeding-up convolutional neural networks ssing fine-tuned CP-decomposition, 2014, 1412.6553.
    • (2014)
    • Lebedev, V.1    Ganin, Y.2    Rakhuba, M.3    Oseledets, I.4    Lempitsky, V.5
  • 80
    • 84937816872 scopus 로고    scopus 로고
    • Combining heterogeneous deep neural networks with conditional random fields for chinese dialogue act recognition
    • Zhou, Y., Hu, Q., Liu, J., Jia, Y., Combining heterogeneous deep neural networks with conditional random fields for chinese dialogue act recognition. Neurocomputing 168 (2015), 408–417.
    • (2015) Neurocomputing , vol.168 , pp. 408-417
    • Zhou, Y.1    Hu, Q.2    Liu, J.3    Jia, Y.4
  • 81
    • 84946222261 scopus 로고    scopus 로고
    • Heterogeneous feature selection with multi-modal deep neural networks and sparse group LASSO
    • Zhao, L., Hu, Q., Wang, W., Heterogeneous feature selection with multi-modal deep neural networks and sparse group LASSO. IEEE Trans. Multimedia 17:11 (2015), 1936–1948.
    • (2015) IEEE Trans. Multimedia , vol.17 , Issue.11 , pp. 1936-1948
    • Zhao, L.1    Hu, Q.2    Wang, W.3
  • 82
    • 84946215753 scopus 로고    scopus 로고
    • Large-margin multi-modal deep learning for RGB-d object recognition
    • Wang, A., Lu, J., Cai, J., Cham, T., Wang, G., Large-margin multi-modal deep learning for RGB-d object recognition. IEEE Trans. Multimedia 17:11 (2015), 1887–1898.
    • (2015) IEEE Trans. Multimedia , vol.17 , Issue.11 , pp. 1887-1898
    • Wang, A.1    Lu, J.2    Cai, J.3    Cham, T.4    Wang, G.5
  • 85
    • 84962019916 scopus 로고    scopus 로고
    • Deep computation model for unsupervised feature learning on big data
    • Zhang, Q., Yang, L.T., Chen, Z., Deep computation model for unsupervised feature learning on big data. IEEE Trans. Serv. Comput. 9:1 (2016), 161–171.
    • (2016) IEEE Trans. Serv. Comput. , vol.9 , Issue.1 , pp. 161-171
    • Zhang, Q.1    Yang, L.T.2    Chen, Z.3
  • 86
    • 84963853667 scopus 로고    scopus 로고
    • Privacy preserving deep computation model on cloud for big data feature learning
    • Zhang, Q., Yang, L.T., Chen, Z., Privacy preserving deep computation model on cloud for big data feature learning. IEEE Trans. Comput. 65:5 (2016), 1351–1362.
    • (2016) IEEE Trans. Comput. , vol.65 , Issue.5 , pp. 1351-1362
    • Zhang, Q.1    Yang, L.T.2    Chen, Z.3
  • 87
    • 0030141870 scopus 로고    scopus 로고
    • Incremental backpropagation learning networks
    • Fu, L.M., Huang, H., Principe, J.C., Incremental backpropagation learning networks. IEEE Trans. Neural Netw. 7:3 (1996), 757–761.
    • (1996) IEEE Trans. Neural Netw. , vol.7 , Issue.3 , pp. 757-761
    • Fu, L.M.1    Huang, H.2    Principe, J.C.3
  • 88
    • 37849186543 scopus 로고    scopus 로고
    • Parameter incremental learning algorithm for neural networks
    • Wan, S., Banta, L.E., Parameter incremental learning algorithm for neural networks. IEEE Trans. Neural Netw. 17:6 (2006), 1424–1438.
    • (2006) IEEE Trans. Neural Netw. , vol.17 , Issue.6 , pp. 1424-1438
    • Wan, S.1    Banta, L.E.2
  • 89
    • 84859418371 scopus 로고    scopus 로고
    • Online learning and online convex optimization
    • Shalev-Shwartz, S., Online learning and online convex optimization. Found. Trends Mach. Learn. 4:2 (2012), 107–194.
    • (2012) Found. Trends Mach. Learn. , vol.4 , Issue.2 , pp. 107-194
    • Shalev-Shwartz, S.1
  • 91
    • 85014899291 scopus 로고    scopus 로고
    • Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos
    • Yu, L., Chen, H., Dou, Q., Qin, J., Heng, P.A., Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE J. Biomed. Health Inf. 21:1 (2017), 65–75.
    • (2017) IEEE J. Biomed. Health Inf. , vol.21 , Issue.1 , pp. 65-75
    • Yu, L.1    Chen, H.2    Dou, Q.3    Qin, J.4    Heng, P.A.5
  • 92
    • 85033491938 scopus 로고    scopus 로고
    • Incremental updating method for big data feature learning
    • Bu, F., Chen, Z., Zhang, Q., Incremental updating method for big data feature learning. Comput. Eng. Appl. 51:12 (2015), 21–26.
    • (2015) Comput. Eng. Appl. , vol.51 , Issue.12 , pp. 21-26
    • Bu, F.1    Chen, Z.2    Zhang, Q.3
  • 94
    • 84973345832 scopus 로고    scopus 로고
    • Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update filtering
    • IEEE
    • Chen, K., Huo, Q., Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update filtering. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2016, IEEE, 5880–5884.
    • (2016) Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing , pp. 5880-5884
    • Chen, K.1    Huo, Q.2
  • 95
    • 84857148573 scopus 로고    scopus 로고
    • The meaningful use of big data: four perspectives – four challenges
    • Bizer, C., Boncz, P., Brodie, M.L., Erling, O., The meaningful use of big data: four perspectives – four challenges. ACM SIGMOD Rec. 40:4 (2012), 56–60.
    • (2012) ACM SIGMOD Rec. , vol.40 , Issue.4 , pp. 56-60
    • Bizer, C.1    Boncz, P.2    Brodie, M.L.3    Erling, O.4
  • 96
    • 84899458050 scopus 로고    scopus 로고
    • Data quality management, data usage experience and acquisition intention of big data analytics
    • Kwon, O., Lee, N., Shi, B., Data quality management, data usage experience and acquisition intention of big data analytics. Int. J. Inf. Manage. 34:3 (2014), 387–394.
    • (2014) Int. J. Inf. Manage. , vol.34 , Issue.3 , pp. 387-394
    • Kwon, O.1    Lee, N.2    Shi, B.3
  • 100
    • 79551480483 scopus 로고    scopus 로고
    • Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion
    • Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P., Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11 (2010), 3371–3408.
    • (2010) J. Mach. Learn. Res. , vol.11 , pp. 3371-3408
    • Vincent, P.1    Larochelle, H.2    Lajoie, I.3    Bengio, Y.4    Manzagol, P.5
  • 101
    • 85033484812 scopus 로고    scopus 로고
    • Incomplete big data impputation algorithm based on deep learning
    • Bu, F., Chen, Z., Zhang, Q., Incomplete big data impputation algorithm based on deep learning. Microelectr. Comput. 31:12 (2014), 173–176.
    • (2014) Microelectr. Comput. , vol.31 , Issue.12 , pp. 173-176
    • Bu, F.1    Chen, Z.2    Zhang, Q.3
  • 102
    • 84963815828 scopus 로고    scopus 로고
    • Non-local auto-encoder with collaborative stabilization for image restoration
    • R, W., Tao, D., Non-local auto-encoder with collaborative stabilization for image restoration. IEEE Trans. Image Process. 25:5 (2016), 2117–2129.
    • (2016) IEEE Trans. Image Process. , vol.25 , Issue.5 , pp. 2117-2129
    • R, W.1    Tao, D.2


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