Randomized Riemannian Preconditioning for Quadratically Constrained Problems

Boris Shustin, Haim Avron

Optimization problem with quadratic equality constraints are prevalent in machine learning. Indeed, two important examples are Canonical Correlation Analysis (CCA) and Linear Discriminant Analysis (LDA). Unfortunately, methods for solving such problems typically involve computing matrix inverses and decomposition. For the aforementioned problems, these matrices are actually Gram matrices of input data matrices, and as such the computations are too expensive for large scale datasets. In this paper, we propose a sketching based approach for solving CCA and LDA that reduces the cost dependence on the input size. The proposed algorithms feature randomized preconditioning combined with Riemannian optimization.

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