A Novel Approach for Outlier Detection and Robust Sensory Data Model Learning

In the past few decades machine learning and data analysis have been having a huge growth and they have been applied in many different problems in the field of robotics. Data are usually the result of sensor measurements and, as such, they might be subjected to noise and outliers. The presence of outliers has a huge impact on modelling the acquired data, resulting in inappropriate models. In this work a novel approach for outlier detection and rejection for input/output mapping in regression problems is presented. The robustness of the method is shown both through simulated data for linear and nonlinear regression, and real sensory data. Despite being validated by using artificial neural networks, the method can be generalized to any other regression method.