Robust learning algorithm based on LTA estimator

Gross errors and outliers in the feedforward neural networks training sets may often corrupt the performance of traditional learning algorithms. Such algorithms try to fit networks to the contaminated data, so the resulting model may be far from the desired one. In this paper we propose new, robust to outliers, learning algorithm based on the concept of the least trimmed absolute value (LTA) estimator. The novel LTA algorithm is compared with traditional approach and other robust learning methods. Experimental results, presented in this article, demonstrate improved performance of the proposed training framework, especially for contaminated training data sets.