Journal:IEEE Transactions on Neural Networks and Learning Systems

Key Words:optimization; encoding; learning systems; correlation; tansforms; data models; semantics

Abstract:Multi-view data can represent objects from different perspectives and thus provide complementary information for data analysis. A topic of great importance in multi-view learning is to locate a low-dimensional latent subspace, where common semantic features are shared by multiple data sets. However, most existing methods ignore uncorrelated items (i.e., view-specific features) and may cause semantic bias during the process of common feature learning. In this article, we propose a non-negative correlated and uncorrelated feature co-learning (CoUFC) method to address this concern. More specifica

Co-author:Tao Yang,Jie Zhang,Zhikui Chen,Z. Jane Wang

First Author:赵亮

Indexed by:Development research

Correspondence Author:杨懿

Document Code:000637534200007

First-Level Discipline:Control Science and Engineering

Document Type:J

Volume:32

Issue:4

Page Number:1486-1496

ISSN No.:2162237X

Translation or Not:no

CN No.:null

Date of Publication:2020-04-29

Included Journals:SCI

Links to published journals:https://ieeexplore-ieee-org-s.vpn.buaa.edu.cn:8118/document/9082119

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