  With the development of information technology, we have witnessed an age of
data explosion which produces a large variety of data filled with redundant
information. Because dimension reduction is an essential tool which embeds
high-dimensional data into a lower-dimensional subspace to avoid redundant
information, it has attracted interests from researchers all over the world.
However, facing with features from multiple views, it's difficult for most
dimension reduction methods to fully comprehended multi-view features and
integrate compatible and complementary information from these features to
construct low-dimensional subspace directly. Furthermore, most multi-view
dimension reduction methods cannot handle features from nonlinear spaces with
high dimensions. Therefore, how to construct a multi-view dimension reduction
methods which can deal with multi-view features from high-dimensional nonlinear
space is of vital importance but challenging. In order to address this problem,
we proposed a novel method named Co-regularized Multi-view Sparse
Reconstruction Embedding (CMSRE) in this paper. By exploiting correlations of
sparse reconstruction from multiple views, CMSRE is able to learn local sparse
structures of nonlinear manifolds from multiple views and constructs
significative low-dimensional representations for them. Due to the proposed
co-regularized scheme, correlations of sparse reconstructions from multiple
views are preserved by CMSRE as much as possible. Furthermore, sparse
representation produces more meaningful correlations between features from each
single view, which helps CMSRE to gain better performances. Various evaluations
based on the applications of document classification, face recognition and
image retrieval can demonstrate the effectiveness of the proposed approach on
multi-view dimension reduction.
