Cited by 1,255Big Data Technologies and ApplicationsStochastic Gradient Optimization TechniquesAdvanced Neural Network Applications

Big data deep learning: Challenges and perspectives

Open access
Chen, Xue-Wen, Lin, Xiaotong·2014·IEEE Access

Abstract

Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. In this paper, we provide a brief overview of deep learning, and highlight current research efforts and the challenges to big data, as well as the future trends.

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Research works printed but not linked: 5

  1. V. Nair and G. Hinton, ''3D object recongition with deep belief nets,'' in Proc. Adv. NIPS, vol. 22. 2009, pp. 1339-1347.
  2. H. Lee, A. Battle, R. Raina, and A. Ng, ''Efficient sparse coding algo- rithms,'' in Proc. Neural Inf. Procees. Syst., 2006, pp. 801-808.
  3. A. Coats, B. Huval, T. Wng, D. Wu, and A. Wu, ''Deep Learn- ing with COTS HPS systems,'' J. Mach. Learn. Res., vol. 28, no. 3, pp. 1337-1345, 2013.
  4. Fact Sheet: Brain Initiative, Office of the Press Secretary, The White House, Washington, DC, USA, 2013.
  5. XUE-WEN CHEN (M'00-SM'03) is currently a Professor and the Chair with the Department of Computer Science, Wayne State University, Detroit, MI, USA. He received the Ph.D. degree from Carnegie Mellon University, Pittsburgh, PA, USA, in 2001. He is currently serving as an Asso- ciate Editor or an Editorial Board Member for sev- eral international journals, including IEEE ACCESS, BMC Systems Biology, and the IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE. He served as a Conference Chair or Program Chair for a number of conferences such as the 21st ACM Conference on Information and Knowledge Man- agement in 2012 and the 10th IEEE International Conference on Machine Learning and Applications in 2011. He is a Senior Member of the IEEE Computer Society.

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Authors
Chen, X.-W., & Lin, X.
Affiliations
  • Wayne State University (US)
  • Oakland University (US)
Venue
IEEE Access
Published
2014
Volume
2
Pages
514-525
ISSN
2169-3536 (ISSN-L)
OpenAlex
W1984020445
Cited by
1,255
References
102
Citations by year
2026: 35 · 2025: 69 · 2024: 107 · 2023: 125 · 2022: 126 · 2021: 156 · 2020: 156 · 2019: 172 · 2018: 144 · 2017: 83 · 2016: 50 · 2015: 31 · 2014: 1
FWCI
102.72
Type
journal article
Language
en
Open access
Locations
  • IEEE Access (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
deep learning, big data, big data challenges, predictive analytics, machine learning, pattern recognition, future trends

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