In this paper, a novel subspace learning method, semi-supervised marginal discriminant analysis (SMDA), is proposed for classification. SMDA aims at maintaining the intrinsic neig...
In this paper, we propose a robust watermarking method for 3-D triangle surface meshes. Most previous methods based on the wavelet analysis can process only semi-regular meshes. Ou...
Many sensor network applications are data-centric, and data analysis plays an important role in these applications. However, it is a challenging task to find out what specific prob...
7 Linear discriminant analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled p...
We simultaneously approach two tasks of nonlinear discriminant analysis and kernel selection problem by proposing a unified criterion, Fisher+Kernel Criterion. In addition, an eff...
Shu Yang, Shuicheng Yan, Dong Xu, Xiaoou Tang, Cha...