In the second part of this lecture, we discuss how the resulting projections can be assessed. Moreover, we discuss how the user can affect the projections. We also discuss a whole workflow of assisted dimension reduction. This relates back to ideas discussed already in the first lecture: to provide guidance and assistance to support analysts in using visual analytics techniques. It turns out that there is not just one best way to perform dimension reduction - there are valid alternatives. Some of them better preserve outliers, others better preserve clusters or indicate correlations. Thus, using a sequence of possible dimension reduction is more likely to fully understand the data.
Chapters:
00:00 - Assessment of Projection Quality
18:11 - Assisted Dimension Reduction
44:26 - Guidance
53:00 - Summary, Outlook and References
