Visual and semantic interpretability of projections of high dimensional data for classification tasks by Ilknur Icke and Andrew Rosenberg.
A number of visual quality measures have been introduced in visual analytics literature in order to automatically select the best views of high dimensional data from a large number of candidate data projections. These methods generally concentrate on the interpretability of the visualization and pay little attention to the interpretability of the projection axes. In this paper, we argue that interpretability of the visualizations and the feature transformation functions are both crucial for visual exploration of high dimensional labeled data. We present a two-part user study to examine these two related but orthogonal aspects of interpretability. We first study how humans judge the quality of 2D scatterplots of various datasets with varying number of classes and provide comparisons with ten automated measures, including a number of visual quality measures and related measures from various machine learning fields. We then investigate how the user perception on interpretability of mathematical expressions relate to various automated measures of complexity that can be used to characterize data projection functions. We conclude with a discussion of how automated measures of visual and semantic interpretability of data projections can be used together for exploratory analysis in classification tasks.
Rather small group of test subjects (20) so I don’t think you can say much other than more work is needed.
Then it occurred to me that I often speak of studies applying to “users” without stopping to remember that for many tasks, I fall into that self-same category. Subject to the same influences, fatigues and even mistakes.
Anyone know of research by researchers being applied to the same researchers?