Multiview subspace learning has attracted much attention due to the efficacy of exploring the information on multiview features. Most existing methods perform data reconstruction on the original feature space and thus are vulnerable to noisy data. In this article, we propose a novel multiview subspace learning method, called multiview consensus structure discovery (MvCSD). Specifically, we learn the low-dimensional subspaces corresponding to different views and simultaneously pursue the structure consensus over subspace clustering for multiple views. In such a way, latent subspaces from different views regularize each other toward a common consensus that reveals the underlying cluster structure. Compared to existing methods, MvCSD leverages the consensus structure derived from the subspaces of diverse views to better exploit the intrinsic complementary information that well reflects the essence of data. Accordingly, the proposed MvCSD is capable of producing a more robust and accurate representation structure which is crucial for multiview subspace learning. The proposed method can be optimized effectively, with theoretical convergence guarantee, by alternatively iterating the argument Lagrangian multiplier algorithm and the eigendecomposition. Extensive experiments on diverse datasets demonstrate the advantages of our method over the state-of-the-art methods.

中文翻译：

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多视图共识结构发现


多视图子空间学习由于探索多视图特征信息的功效而备受关注。大多数现有方法在原始特征空间上进行数据重建，因此容易受到噪声数据的影响。在本文中，我们提出了一种新颖的多视图子空间学习方法，称为多视图一致性结构发现（MvCSD）。具体来说，我们学习不同视图对应的低维子空间，同时追求多视图子空间聚类的结构共识。通过这种方式，来自不同视图的潜在子空间相互规范，以达成揭示潜在簇结构的共同共识。与现有方法相比，MvCSD 利用源自​​不同视图子空间的共识结构，更好地利用反映数据本质的内在互补信息。因此，所提出的 MvCSD 能够产生更稳健和更准确的表示结构，这对于多视图子空间学习至关重要。通过交替迭代参数拉格朗日乘子算法和特征分解，可以有效地优化该方法，并保证理论上的收敛性。对不同数据集的广泛实验证明了我们的方法相对于最先进的方法的优势。