STRUCTURE PRESERVING MULTI-VIEW DIMENSIONALITY REDUCTION
Zhan Wang, Lizhi Wang, Hua Huang
-
Members: FreeSPS
IEEE Members: $11.00
Non-members: $15.00Length: 11:05
09 Jul 2020
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.
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.
