Smoothing Scatterplots
Wendy L. Martinez, Angel R. Martinez, Jeffrey Solka, Angel Martinez · Chapman & Hall/CRC series in computer science & data analysis/Series in computer science and data analysis · 2010
This chapter covers various methods for nonlinear dimensionality reduction, where the nonlinear aspect refers to the mapping between the highdimensional space and the low-dimensional space. We start off by discussing a method that has been around for many years called multidimensional scaling. We follow this with several more recently developed nonlinear dimensionality reduction techniques called locally linear embedding, isometric feature mapping, and Hessian eigenmaps. We conclude by discussing some methods from the machine learning community called selforganizing maps, generative topographic maps, and curvilinear component analysis.