Multiview learning has received extensive research interest and has demonstrated promising results in recent years. Despite the progress made, there are two significant challenges within multiview learning. First, some of the existing methods directly use original features to reconstruct data points without considering the issue of feature redundancy. Second, existing methods cannot fully exploit the complementary information across multiple views and meanwhile preserve the view-specific properties; therefore, the degraded learning performance will be generated. To address the above issues, we propose a novel multiview latent space learning framework with feature redundancy minimization. We aim to learn a latent space to mitigate the feature redundancy and use the learned representation to reconstruct every original data point. More specifically, we first project the original features from multiple views onto a latent space, and then learn a shared dictionary and view-specific dictionaries to, respectively, exploit the correlations across multiple views as well as preserve the view-specific properties. Furthermore, the Hilbert-Schmidt independence criterion is adopted as a diversity constraint to explore the complementarity of multiview representations, which further ensures the diversity from multiple views and preserves the local structure of the data in each view. Experimental results on six public datasets have demonstrated the effectiveness of our multiview learning approach against other state-of-the-art methods.

中文翻译：

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具有特征冗余最小​​化的多视图潜在空间学习


近年来，多视图学习引起了广泛的研究兴趣，并取得了可喜的成果。尽管取得了进展，但多视图学习仍面临两个重大挑战。首先，现有的一些方法直接使用原始特征来重建数据点，没有考虑特征冗余的问题。其次，现有方法无法充分利用多个视图之间的互补信息，同时保留视图特定的属性；因此，将会产生学习成绩下降的情况。为了解决上述问题，我们提出了一种具有特征冗余最小​​化的新颖的多视图潜在空间学习框架。我们的目标是学习潜在空间以减轻特征冗余，并使用学习到的表示来重建每个原始数据点。更具体地说，我们首先将多个视图中的原始特征投影到潜在空间上，然后学习共享字典和特定于视图的字典，以分别利用多个视图之间的相关性并保留特定于视图的属性。此外，采用希尔伯特-施密特独立性准则作为多样性约束来探索多视图表示的互补性，这进一步保证了多个视图的多样性并保留了每个视图中数据的局部结构。六个公共数据集的实验结果证明了我们的多视图学习方法相对于其他最先进方法的有效性。