Multi-view predictive latent space learning

Jirui Yuan, Ke Gao, Pengfei Zhu, Karen Egiazarian

    Research output: Contribution to journalArticleScientificpeer-review

    1 Citation (Scopus)


    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.

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


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

    Publication forum classification

    • Publication forum level 2

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Computer Vision and Pattern Recognition
    • Artificial Intelligence


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