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In this paper, we derive new model formulations for computing generalizedsingular values of a Grassman matrix pair. These new formulations make use oftruncated filter matrices to locate the $i$-th generalized singular value of aGrassman matrix pair. The resulting matrix optimization problems can be solvedby using numerical methods involving Newton's method on Grassmann manifold.Numerical examples on synthetic data sets and gene expression data sets arereported to demonstrate the high accuracy and the fast computation of theproposed new ormulations for computing arbitrary generalized singular value ofGrassman matrix pair.

New Formulation and Computation for Generalized Singular Values of Grassman Matrix Pair