Subspace learning

Linear time Principal Component Pursuit and its extensions using l1 Filtering

We propose a novel algorithm, called ℓ1 filtering, for exactly solving PCP with an complexity, where m×n is the size of data matrix and r is the rank of the matrix to recover, which is supposed to be much smaller than m and n.

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.