Structure preserving multi-view dimensionality reduction

The multi-view features from multimedia data in the real-world are usually high-dimensional. How to simultaneously reduce their dimensions and explore the complementary information among multi-view features is of vital importance but challenging. In this paper, we propose a novel unsupervised method named structure preserving multi-view dimensionality reduction (SPMDR). We first propose a bilinear low-rank representation with an orthogonal constraint in the learning subspace. Then, we construct a 3-order rotated tensor among the low-rank coefficient matrices and utilize tensor nuclear norm to capture complementary information among multi-view representations. Finally, we develop a numerical algorithm for solving the proposed model. Our method is robust to noisy data and can capture the complex correlations among multi-view features. Experimental results on recognition tasks demonstrate the superior performance of SPMDR.