Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering

• URL: http://arxiv.org/abs/2106.11232v1
• Date: Mon, 21 Jun 2021 16:23:28 GMT
• Title: Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering
• Authors: Jie Xu, Yazhou Ren, Huayi Tang, Xiaorong Pu, Xiaofeng Zhu, Ming Zeng, Lifang He
• Abstract summary: We present a novel VAE-based multi-view clustering framework (Multi-VAE) We define a view-common variable and multiple view-peculiar variables in the generative model. By controlling the mutual information capacity to disentangle the view-common and view-peculiar representations, continuous visual information of multiple views can be separated.
• Score: 20.412896884905496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view clustering, a long-standing and important research problem, focuses on mining complementary information from diverse views. However, existing works often fuse multiple views' representations or handle clustering in a common feature space, which may result in their entanglement especially for visual representations. To address this issue, we present a novel VAE-based multi-view clustering framework (Multi-VAE) by learning disentangled visual representations. Concretely, we define a view-common variable and multiple view-peculiar variables in the generative model. The prior of view-common variable obeys approximately discrete Gumbel Softmax distribution, which is introduced to extract the common cluster factor of multiple views. Meanwhile, the prior of view-peculiar variable follows continuous Gaussian distribution, which is used to represent each view's peculiar visual factors. By controlling the mutual information capacity to disentangle the view-common and view-peculiar representations, continuous visual information of multiple views can be separated so that their common discrete cluster information can be effectively mined. Experimental results demonstrate that Multi-VAE enjoys the disentangled and explainable visual representations, while obtaining superior clustering performance compared with state-of-the-art methods.

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