Visualization of multidimensional data is a difficult task, for which there are many tools. Among these tools, dimensionality reduction methods were shown to be particularly helpful to explore data visually. Techniques with good visual separation are very popular, such as those from the SNE-class, but those often are computationally expensive and non-parametric. An approach based on neural networks was recently proposed to address those shortcomings, but it introduces some fuzziness in the generated projection, which is not desired. In this paper we thoroughly explain the parameter space of this neural network approach and propose a new neighborhood-based learning paradigm, which further improves the quality of the projections learned by the neural networks, and we illustrate our approach on large real-world datasets
