  During the last decades, we have witnessed a surge of interests of learning a
low-dimensional space with discriminative information from one single view.
Even though most of them can achieve satisfactory performance in some certain
situations, they fail to fully consider the information from multiple views
which are highly relevant but sometimes look different from each other.
Besides, correlations between features from multiple views always vary greatly,
which challenges multi-view subspace learning. Therefore, how to learn an
appropriate subspace which can maintain valuable information from multi-view
features is of vital importance but challenging. To tackle this problem, this
paper proposes a novel multi-view dimension reduction method named Multi-view
Locality Low-rank Embedding for Dimension Reduction (MvL2E). MvL2E makes full
use of correlations between multi-view features by adopting low-rank
representations. Meanwhile, it aims to maintain the correlations and construct
a suitable manifold space to capture the low-dimensional embedding for
multi-view features. A centroid based scheme is designed to force multiple
views to learn from each other. And an iterative alternating strategy is
developed to obtain the optimal solution of MvL2E. The proposed method is
evaluated on 5 benchmark datasets. Comprehensive experiments show that our
proposed MvL2E can achieve comparable performance with previous approaches
proposed in recent literatures.
