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arXiv · 2303.06329

MetaViewer: Towards A Unified Multi-View Representation

Ren Wang·Haoliang Sun·Yuling Ma·Xiaoming Xi·Yilong Yin
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Abstract

Existing multi-view representation learning methods typically follow a specific-to-uniform pipeline, extracting latent features from each view and then fusing or aligning them to obtain the unified object representation. However, the manually pre-specify fusion functions and view-private redundant information mixed in features potentially degrade the quality of the derived representation. To overcome them, we propose a novel bi-level-optimization-based multi-view learning framework, where the representation is learned in a uniform-to-specific manner. Specifically, we train a meta-learner, namely MetaViewer, to learn fusion and model the view-shared meta representation in outer-level optimization. Start with this meta representation, view-specific base-learners are then required to rapidly reconstruct the corresponding view in inner-level. MetaViewer eventually updates by observing reconstruction processes from uniform to specific over all views, and learns an optimal fusion scheme that separates and filters out view-private information. Extensive experimental results in downstream tasks such as classification and clustering demonstrate the effectiveness of our method.

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Ren Wang, Haoliang Sun, Yuling Ma, Xiaoming Xi, Yilong Yin. 2023-03-11. MetaViewer: Towards A Unified Multi-View Representation. https://arxiv.org/abs/2303.06329

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