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Multi-view predictive latent space learning

Research output: Contribution to journalArticleScientificpeer-review


Original languageEnglish
JournalPattern Recognition Letters
Early online date2018
Publication statusPublished - 2018
Publication typeA1 Journal article-refereed


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 multi-view 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.


  • Multi-view learning, Predictive latent space learning, Unsupervised clustering, Unsupervised dimension reduction

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