MAKING A LOW-DIMENSIONAL REPRESENTATION SUITABLE FOR DIVERSE TASKS
by Nathan Intrator and Shimon Edelman
We introduce a new approach to the training of classifiers for performance on multiple tasks. The proposed hybrid training method leads to improved generalization via a better low-dimensional representation of the problem space. The quality of the representation is assessed by embedding it in a 2D space using multidimensional scaling, allowing a direct visualization of the results. The performance of the approach is demonstrated on a highly nonlinear image classification task.