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With the development of information technology, we have witnessed an age ofdata explosion which produces a large variety of data filled with redundantinformation. Because dimension reduction is an essential tool which embedshigh-dimensional data into a lower-dimensional subspace to avoid redundantinformation, it has attracted interests from researchers all over the world.However, facing with features from multiple views, it's difficult for mostdimension reduction methods to fully comprehended multi-view features andintegrate compatible and complementary information from these features toconstruct low-dimensional subspace directly. Furthermore, most multi-viewdimension reduction methods cannot handle features from nonlinear spaces withhigh dimensions. Therefore, how to construct a multi-view dimension reductionmethods which can deal with multi-view features from high-dimensional nonlinearspace is of vital importance but challenging. In order to address this problem,we proposed a novel method named Co-regularized Multi-view SparseReconstruction Embedding (CMSRE) in this paper. By exploiting correlations ofsparse reconstruction from multiple views, CMSRE is able to learn local sparsestructures of nonlinear manifolds from multiple views and constructssignificative low-dimensional representations for them. Due to the proposedco-regularized scheme, correlations of sparse reconstructions from multipleviews are preserved by CMSRE as much as possible. Furthermore, sparserepresentation produces more meaningful correlations between features from eachsingle view, which helps CMSRE to gain better performances. Various evaluationsbased on the applications of document classification, face recognition andimage retrieval can demonstrate the effectiveness of the proposed approach onmulti-view dimension reduction.

Co-regularized Multi-view Sparse Reconstruction Embedding for Dimension Reduction