Trace ratio optimization with an application to multi-view learning

Abstract

A trace ratio optimization problem over the Stiefel manifold is investigated from the perspectives of both theory and numerical computations. Necessary conditions in the form of nonlinear eigenvalue problem with eigenvector dependency (NEPv) are established and a numerical method based on the self-consistent field (SCF) iteration with a postprocessing step is designed to solve the NEPv and the method is proved to be always convergent. As an application to multi-view subspace learning, a new framework and its instantiated concrete models are proposed and demonstrated on real world data sets. Numerical results show that the efficiency of the proposed numerical methods and effectiveness of the new orthogonal multi-view subspace learning models.