Department of Computer Science & Engineering

We develop an efficient optimization procedure for learning linear transformations of data to produce structured outputs, e.g., nearest-neighbor rankings or connectivity graphs induced by distance. The proposed method solves a semi-definite programming problem by reducing to a sequence of small quadratic programs, resulting in significant reductions in training complexity. Experiments demonstrate that the proposed method is robust, efficient, and outperforms alternative methods in high-noise settings.

Pre-2018 CSE ID: CS2012-0979