  We propose a novel exemplar selection approach based on Principal Component
Analysis (PCA) and median sampling, and a neural network training regime in the
setting of class-incremental learning. This approach avoids the pitfalls due to
outliers in the data and is both simple to implement and use across various
incremental machine learning models. It also has independent usage as a
sampling algorithm. We achieve better performance compared to state-of-the-art
methods.
