  During the last decades, learning a low-dimensional space with discriminative
information for dimension reduction (DR) has gained a surge of interest.
However, it's not accessible for these DR methods to achieve satisfactory
performance when facing the features from multiple views. In multi-view
learning problems, one instance can be represented by multiple heterogeneous
features, which are highly related but sometimes look different from each
other. In addition, correlations between features from multiple views always
vary greatly, which challenges the capability of multi-view learning methods.
Consequently, constructing a multi-view learning framework with generalization
and scalability, which could take advantage of multi-view information as much
as possible, is extremely necessary but challenging. To implement the above
target, this paper proposes a novel multi-view learning framework based on
similarity consensus, which makes full use of correlations among multi-view
features while considering the scalability and robustness of the framework. It
aims to straightforwardly extend those existing DR methods into multi-view
learning domain by preserving the similarity between different views to capture
the low-dimensional embedding. Two schemes based on pairwise-consensus and
centroid-consensus are separately proposed to force multiple views to learn
from each other and then an iterative alternating strategy is developed to
obtain the optimal solution. The proposed method is evaluated on 5 benchmark
datasets and comprehensive experiments show that our proposed multi-view
framework can yield comparable and promising performance with previous
approaches proposed in recent literatures.
