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Multi -view predictive latent space learning
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DOI：10.1016/j.patrec.2018.06.022.png)
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Abstract
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In unsupervised circumstances, multi-view learning seeks a shared latent representation by taking the consensus and complementary principles into account. However, most existing multi-view unsupervised learning approaches do not explicitly lay stress on the predictability of the latent space. In this paper, we propose a novel multi-view predictive latent space learning (MVP) model and apply it to multi-view clustering and unsupervised dimension reduction. The latent space is forced to be predictive by maximizing the correlation between the latent space and feature space of each view. By learning a multiview graph with adaptive view-weight learning, MVP effectively combines the complementary information from multi-view data. Experimental results on benchmark datasets show that MVP outperforms the state-of-the-art multi-view clustering and unsupervised dimension reduction algorithms. (c) 2018 Elsevier B.V. All rights reserved.
Keywords:
Multi-view learning
Predictive latent space learning
Unsupervised clustering
Unsupervised dimension reduction
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