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Cited by 894

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A survey of machine learning for big data processing

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Qiu, Junfei, Wu, Qihui, Ding, Guoru, Xu, Yuhua, Feng, Shuo·2016·EURASIP Journal on Advances in Signal Processing
DOI 10.1186/s13634-016-0355-x
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Abstract

There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.

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Cited by

Metadata

Authors
Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S.
Venue
EURASIP Journal on Advances in Signal Processing
Publisher
Springer Science and Business Media LLC
Published
2016-05-28
Volume
2016
Issue
1
ISSN
  • 1110-8657 (ISSN-L)
  • 1687-6180
DOI
10.1186/s13634-016-0355-x
OpenAlex
W2406349003
Cited by
894
References
7
FWCI
101.10
Type
journal article
Language
en
Update notices
correction (2016-08-01)
Open access
gold
Licence
creativecommons.org/licenses/by/4.0 (unspecified)
Funders
National Natural Science Foundation of China (CN) (No. 61301160, No. 61172062)

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