Primal space sparse kernel partial least squares regression for large scale problems

Kernel based methods suffer from exceeding time and memory requirements when applied on large datasets since the involved optimization problems typically scale polynomially in the number of data samples. As a remedy we propose both working on a reduced set (for fast evaluation) and at the same time keeping the number of model parameters small (for fast training). Departing from the Nystrom based feature approximation we describe fixed-size least squares support vector machine in the context of primal space least squares regression, to extend it with a supervised counterpart, sparse kernel partial least squares. The model is illustrated on a large scale example.