Probabilistic NeuroScale for Uncertainty Visualisation

This paper is a study of low dimensional visualisation methods for data visualisation under uncertainty of the input data. It focuses on NeuroScale, the feed-forward neural networks algorithm by trying to make the algorithm able to accommodate the uncertainty. The standard model is shown not to work well under high levels of noise within the data and need to be modified. The modifications of the model are verified by using synthetic data to show their ability to accommodate the noise.