Real life data often includes information from different channels. For
example, in computer vision, we can describe an image using different image
features, such as pixel intensity, color, HOG, GIST feature, SIFT features,
etc.. These different aspects of the same objects are often called multi-view
(or multi-modal) data. Low-rank regression model has been proved to be an
effective learning mechanism by exploring the low-rank structure of real life
data. But previous low-rank regression model only works on single view data. In
this paper, we propose a multi-view low-rank regression model by imposing
low-rank constraints on multi-view regression model. Most importantly, we
provide a closed-form solution to the multi-view low-rank regression model.
Extensive experiments on 4 multi-view datasets show that the multi-view
low-rank regression model outperforms single-view regression model and reveals
that multi-view low-rank structure is very helpful.

在计算机视觉中，我们可以使用像素强度、颜色、HOG、GIST 特征、SIFT 特征等不同的图像特征来描述图像。本文提出了一种多视角低秩回归模型，通过对多视角回归模型施加低秩约束，提供了多视角低秩回归模型的闭合解决方案，并在 4 个多视角数据集上进行广泛的实验，结果表明，多视角低秩回归模型优于单视角回归模型，并揭示了多视角低秩结构的辅助作用。