PUBLICATION DATE

2016-05-28

AUTHORS

Guoru Ding, Shuo Feng, Qihui Wu, Yuhua Xu, Junfei Qiu

TITLE

A survey of machine learning for big data processing

ISSUE

1

VOLUME

2016

ISSN (print)

N/A

ISSN (electronic)

1687-6180

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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38 TRIPLES      31 PREDICATES      37 URIs      21 LITERALS

Subject Predicate Object
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30 sg:publicationDate 2016-05-28
31 sg:publicationYear 2016
32 sg:publicationYearMonth 2016-05
33 sg:scigraphId 11a70819b51ee578bb75792c926d4a71
34 sg:title A survey of machine learning for big data processing
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