Learning Weights in Discrimination Functions Using a priori Constraints

We introduce a learning algorithm for the weights in a very common class of discrimination functions usually called \weighted average". Diierent submodules are produced by some feature extraction and are weighted according to their signiicance for the actual discrimination task. The learning algorithm can reduce the number of free variables by simple but eeective a priori criteria about signiicant features. We apply our algorithm to three diierent tasks all concerned with face recognition: a 40 dimensional and an 1800 dimensional problem in face discrimination, and a 42 dimensional problem in pose estimation. For the rst and second task, the same weights are applied to the discrimination of all classes; for the third problem, a metric for every class is learned. For all tasks signiicant improvements could be achieved. In the third task the performance was increased from 80% to 90%. The idea of our algorithm is so general that it can be applied to improve a large number of existing pattern recognition systems.