We introduce a regularized MRW learning model, using a low-rank penalty to constrain the global subspace structure, for subspace clustering and estimation.
We develop a supervised dimensionality reduction method, called Lorentzian discriminant projection(LDP), for feature extraction and classification
We propose a fast algorithm for solving the KFSODVs, which is based on rank-one update (ROU) of the eigensytems.
We develops a supervised dimensionality reduction method, Lorentzian Discriminant Projection (LDP), for discriminant analysis and classification. Our method represents the structures of sample data by a manifold, which is furnished with a Lorentzian metric tensor.