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发表刊物：IEEE Transactions on Neural Networks and Learning Systems
关键字：optimization; encoding; learning systems; correlation; tansforms; data models; semantics
摘要：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
合写作者：Tao Yang,Jie Zhang,Zhikui Chen,Z. Jane Wang
第一作者：赵亮
论文类型：开发研究
通讯作者：杨懿
论文编号：000637534200007
一级学科：控制科学与工程
文献类型：期刊
卷号：32
期号：4
页面范围：1486-1496
ISSN号：2162237X
是否译文：否
CN号：null
发表时间：2020-04-29
收录刊物：SCI
发布期刊链接：https://ieeexplore-ieee-org-s.vpn.buaa.edu.cn:8118/document/9082119