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A survey of multi-view machine learning

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Abstract

Multi-view learning or learning with multiple distinct feature sets is a rapidly growing direction in machine learning with well theoretical underpinnings and great practical success. This paper reviews theories developed to understand the properties and behaviors of multi-view learning and gives a taxonomy of approaches according to the machine learning mechanisms involved and the fashions in which multiple views are exploited. This survey aims to provide an insightful organization of current developments in the field of multi-view learning, identify their limitations, and give suggestions for further research. One feature of this survey is that we attempt to point out specific open problems which can hopefully be useful to promote the research of multi-view machine learning.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China under Project 61075005, the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, and Shanghai Knowledge Service Platform for Trustworthy Internet of Things (No. ZF1213).

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  1. Department of Computer Science and Technology, East China Normal University, 500 Dongchuan Road, Shanghai, 200241, China

    Shiliang Sun

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Correspondence to Shiliang Sun.

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Sun, S. A survey of multi-view machine learning. Neural Comput & Applic 23, 2031–2038 (2013). https://doi.org/10.1007/s00521-013-1362-6

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