Uncertainty-Aware Multi-View Representation Learning

• URL: http://arxiv.org/abs/2201.05776v1
• Date: Sat, 15 Jan 2022 07:16:20 GMT
• Title: Uncertainty-Aware Multi-View Representation Learning
• Authors: Yu Geng, Zongbo Han, Changqing Zhang, Qinghua Hu
• Abstract summary: We devise a novel unsupervised multi-view learning approach, termed as Dynamic Uncertainty-Aware Networks (DUA-Nets) Guided by the uncertainty of data estimated from the generation perspective, intrinsic information from multiple views is integrated to obtain noise-free representations. Our model achieves superior performance in extensive experiments and shows the robustness to noisy data.
• Score: 53.06828186507994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning from different data views by exploring the underlying complementary information among them can endow the representation with stronger expressive ability. However, high-dimensional features tend to contain noise, and furthermore, the quality of data usually varies for different samples (even for different views), i.e., one view may be informative for one sample but not the case for another. Therefore, it is quite challenging to integrate multi-view noisy data under unsupervised setting. Traditional multi-view methods either simply treat each view with equal importance or tune the weights of different views to fixed values, which are insufficient to capture the dynamic noise in multi-view data. In this work, we devise a novel unsupervised multi-view learning approach, termed as Dynamic Uncertainty-Aware Networks (DUA-Nets). Guided by the uncertainty of data estimated from the generation perspective, intrinsic information from multiple views is integrated to obtain noise-free representations. Under the help of uncertainty, DUA-Nets weigh each view of individual sample according to data quality so that the high-quality samples (or views) can be fully exploited while the effects from the noisy samples (or views) will be alleviated. Our model achieves superior performance in extensive experiments and shows the robustness to noisy data.

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