Most real data sets contain atypical observations, often referred to as outliers. Their presence may have a negative impact in data modeling using machine learning. This is particularly the case in data density estimation approaches. Manifold learning techniques provide low-dimensional data representations, often oriented towards visualization. The visualization provided by density estimation manifold learning methods can be compromised by the presence of outliers. Recently, a cartogram-based representation of model-generated distortion was presented for nonlinear dimensionality reduction. Here, we investigate the impact of outliers on this visualization when using manifold learning techniques that behave robustly in their presence.