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.

在无人监督的情况下，多视图学习通过考虑共识和补充原则来寻求共享的潜在表示。但是，大多数现有的多视图无监督学习方法并未明确强调潜在空间的可预测性。在本文中，我们提出了一种新颖的多视图预测潜在空间学习（MVP）模型，并将其应用于多视图聚类和无监督降维。通过最大化每个视图的潜在空间和特征空间之间的相关性，可以迫使潜在空间具有可预测性。通过使用自适应视图权重学习来学习多视图图，MVP有效地组合了来自多视图数据的补充信息。