Non-negative matrix factorization is used to find a basic and weight approximate the non-negative matrix. It has proven be powerful low-rank decomposition technique for multivariate data. However, its performance largely depends on assumption of fixed number features. This work proposes new probabilistic which factorizes into factor with 0,1 constraints In order automatically learn potential bi...

Non-negative Matrix Factorization (NMF) is a part-based image representation method which adds a non-negativity constraint to matrix factorization. NMF is compatible with the intuitive notion of combining parts to form a whole face. In this paper, we propose a framework of face recognition by adding NMF constraint and classifier constraints to matrix factorization to get both intuitive features...

Since it is well-known that the Vandermonde matrix is ill-conditioned, while the interpolation itself is not unstable in function space, this paper surveys the choices of other new bases. These bases are data-dependent and are categorized into discretely l2-orthonormal and continuously L2-orthonormal bases. The first one construct a unitary Gramian matrix in the space l2(X) while the late...