基于双重低秩分解的不完整多视图子空间学习

多视图数据在现实世界中应用广泛，各种视角和不同的传感器有助于更好的数据表示，然而，来自不同视图的数据具有较大的差异，尤其当多视图数据不完整时，可能导致训练效果较差甚至失败。为了解决该问题，本文提出了一个基于双重低秩分解的不完整多视图子空间学习算法。所提算法通过两方面来解决不完整多视图问题:一方面，基于双重低秩分解子空间框架，引入潜在因子来挖掘多视图数据中缺失的信息；另一方面，通过预先学习的多视图数据低维特征获得更好的鲁棒性，并以有监督的方式来指导双重低秩分解。实验结果证明，所提算法较之前的多视图子空间学习算法有明显优势；即使对于不完整的多视图数据，该算法也具有良好的分类性能。

Incomplete multi-view subspace learning through dual low-rank decompositions

Multi-view data are very common in real-world applications. Different viewpoints and sensors tend to facilitate better data representation. However, data from various perspectives show a significant variation. Especially when only incomplete multi-view data are available, the corresponding multi-view learning may result in poor performance or even training failure. This study proposes a multi-view learning algorithm called IMSL (Incomplete Multi-View Subspace Learning through Dual Low-Rank Decompositions) to tackle this issue. The proposed algorithm addresses the incomplete multi-view problem in two ways: (1) Latent factors are introduced into a dual low-rank decomposition subspace framework to mine missing information in the multi-view data. (2) IMSL seeks a more robust subspace through pre-learned low-dimensional features of multi-view data. Furthermore, the supervised data are used to guide dual low-rank decompositions. Experimental results show that the proposed algorithm outperforms the previous multi-view subspace learning algorithms on the adopted incomplete multi-view datasets. Pre-learning low-dimensional features of multi-view data, on the other hand, can improve robustness, and dual low-rank decomposition can be guided in a supervised manner.