Manifold learning

Geodesic based semi-supervised multi-manifold feature extraction

We have proposed a new multi-manifold learning algorithm, which combines semi-supervised multimanifold modeling, nonlinear feature extraction and a new semi-supervised discriminant analysis method to achieve better performance in geodesic feature extraction and classification tasks.

Multi-output regression on the output manifold

In this paper, we consider the output space as a Riemannian submanifold to incorporate its geometric structure into the regression process.

Classification via Semi-Riemannian Spaces

We develop a geometric framework for linear or nonlinear discriminant subspace learning and classification. In our framework, the structures of classes are conceptualized as a semi-Riemannian manifold which is considered as a submanifold embedded in an ambient semi-Riemannian space.