Linear discriminant analysis (LDA) is a popular face recognition technique. However, an inherent problem with this technique stems from the parametric nature of the scatter matrix...
We present a unified duality view of several recently emerged spectral methods for nonlinear dimensionality reduction, including Isomap, locally linear embedding, Laplacian eigenm...
This paper adresses the variance quantification problem for system identification based on the prediction error framework. The role of input and model class selection for the auto-...
We introduce the posterior probabilistic clustering (PPC), which provides a rigorous posterior probability interpretation for Nonnegative Matrix Factorization (NMF) and removes th...
Abstract. We investigate the theories LA, LAP, LAP of linear algebra, which were originally defined to study the question of whether commutativity of matrix inverses has polysize F...