Many different evaluation measures for dimensionality reduction can be summarized based on the co-ranking framework [6] . Here, we extend this framework in two ways: (i) we show that the current parameterization of the quality shows unpredictable behavior, even in simple settings, and we propose a different parameterization which yields more intuitive results; (ii) we propose how to link the quality to point-wise quality measures which can directly be integrated into the visualization.
For instance, point-wise quality measures can be augmented in the visualization to highlight erroneous local regions REF .
Visualizing the quality of dimensionality reduction
ESANN
Computer Science