A novel semi-supervised learning for face recognition

• We points out the shortcoming of LE in learning the local structure.
• Our approach characterizes both the diversity and similarity of data.
• Our approach helps encode the local discriminating information of data.

Laplacian embedding (LE) has been widely used to learn the intrinsic structure of data. However, LE ignores the diversity and may impair the local topology of data, resulting in unstable and inexact intrinsic structure representation. In this article, we build an objective function to learn the intrinsic structure that well characterizes both the similarity and diversity of data, and then incorporate this structure representation into linear discriminant analysis to build a semi-supervised approach, called stable semi-supervised discriminant learning (SSDL). Experimental results on two databases demonstrate the effectiveness of our approach.