 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 spatial quality and stability.
<meaning-changed>  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 spatial quality and stability.
 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 (SPC) method to compute such orderings, which is explicitly parameterized for stability, allowing a trade-off between the spatial quality and stability.