期刊
NEURAL COMPUTING & APPLICATIONS
卷 23, 期 7-8, 页码 2031-2038出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-013-1362-6
关键词
Multi-view learning; Statistical learning theory; Canonical correlation analysis; Co-training; Co-regularization
资金
- National Natural Science Foundation of China [61075005]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry
- Shanghai Knowledge Service Platform for Trustworthy Internet of Things [ZF1213]
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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