Structure learning with similarity preserving

Zhao Kang, Xiao Lu, Yiwei Lu, Chong Peng, Wenyu Chen, Zenglin Xu

Abstract: Highlights•We preserve the pairwise relations during low-dimensional structure learning process.•Combining with auto-encoder, a more discriminative latent representation is achieved.•Evaluations on shallow and deep models with clustering and classification tasks.•Our framework can be readily applied to other self-expression methods and tasks.