Before revision:  We introduce stable techniquesbased on well-established dimensionality-reduction techniques : Principle Component Analysis, Sammon mapping, and t-SNE. Our Stable Principal Component method is explicitly parametrized for stability, allowing a trade-off between the two quality criteria .
After revision:  Previous work has considered only the creation of 1D orderings, using spatial subdivisions and clustering techniques. In contrast, we propose to use actual dimensionality-reduction techniques : Principle Component Analysis, Sammon mapping, and t-SNE. Our Stable Principal Component method is explicitly parametrized for stability, allowing a trade-off between the two quality criteria .