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:  We introduce stable techniquesbased on well-established dimensionality-reduction techniques to compute stable and spatially informative 1D representations. These more general 1D representations provide the user with additional visual cues describing the spatial distribution of the data, and naturally imply also a 1D ordering. To make dimensionality-reduction techniques suitable for visual summaries, we introduce stable variants of 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 .