期刊
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
卷 32, 期 7, 页码 4202-4210出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2021.3127007
关键词
Correlation; Tensors; Uniform resource locators; Task analysis; Feature extraction; Collaboration; Kernel; Multi-view learning; collaborative learning; multi-view representation learning
资金
- National Key Research and Development Program of China [2020AAA0109602]
- Key Research and Development Program of Shaanxi [2021GXLH-Z-097]
- Fundamental Research Funds for Central Universities [xzy012019045, xzy022020050]
This paper focuses on the challenging problem of unsupervised multi-view representation learning and introduces a novel method, Collaborative Unsupervised Multi-view Representation Learning (CUMRL), which benefits from the high-order view correlations of multi-view data by introducing a collaborative learning strategy. Experiments demonstrate the effectiveness and competitiveness of the multi-view representation achieved by the proposed method for different learning tasks.
In this paper, we delve into the challenging problem in multi-view learning, namely unsupervised multi-view representation learning, the goal of which is to effectively integrate information from multiple views and learn the unified feature representation with comprehensive information in an unsupervised manner. Despite the progress attained in recent years, it is still a challenging issue since the correlations across multiple views are complex and difficult to model during the learning process, especially in the absence of label information. To address this problem, we introduce a novel method, termed Collaborative Unsupervised Multi-view Representation Learning (CUMRL), which benefits from the high-order view correlations of multi-view data by introducing a collaborative learning strategy. Specifically, the low-rank tensor constraint is employed and plays the role of a bridge, which links the view-specific compact learning and unified representation learning in CUMRL. Experiments demonstrate the effectiveness and competitiveness of the multi-view representation achieved by the proposed method for different learning tasks, compared to several state-of-the-art methods.
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