Part of Advances in Neural Information Processing Systems 22 (NIPS 2009)
Eric Garcia, Maya Gupta
We present a new empirical risk minimization framework for approximating functions from training samples for low-dimensional regression applications where a lattice (look-up table) is stored and interpolated at run-time for an efficient hardware implementation. Rather than evaluating a fitted function at the lattice nodes without regard to the fact that samples will be interpolated, the proposed lattice regression approach estimates the lattice to minimize the interpolation error on the given training samples. Experiments show that lattice regression can reduce mean test error compared to Gaussian process regression for digital color management of printers, an application for which linearly interpolating a look-up table (LUT) is standard. Simulations confirm that lattice regression performs consistently better than the naive approach to learning the lattice, particularly when the density of training samples is low.