  With the development of feature extraction technique, one sample always can
be represented by multiple features which locate in high-dimensional space.
Multiple features can re ect various perspectives of one same sample, so there
must be compatible and complementary information among the multiple views.
Therefore, it's natural to integrate multiple features together to obtain
better performance. However, most multi-view dimension reduction methods cannot
handle multiple features from nonlinear space with high dimensions. To address
this problem, we propose a novel multi-view dimension reduction method named
Multi-view Reconstructive Preserving Embedding (MRPE) in this paper. MRPE
reconstructs each sample by utilizing its k nearest neighbors. The similarities
between each sample and its neighbors are primely mapped into lower-dimensional
space in order to preserve the underlying neighborhood structure of the
original manifold. MRPE fully exploits correlations between each sample and its
neighbors from multiple views by linear reconstruction. Furthermore, MRPE
constructs an optimization problem and derives an iterative procedure to obtain
the low-dimensional embedding. Various evaluations based on the applications of
document classification, face recognition and image retrieval demonstrate the
effectiveness of our proposed approach on multi-view dimension reduction.
