  Dimensionality reduction (DR) methods are commonly used for analyzing and
visualizing multidimensional data. However, when data is a live streaming feed,
conventional DR methods cannot be directly used because of their computational
complexity and inability to preserve the projected data positions at previous
time points. In addition, the problem becomes even more challenging when the
dynamic data records have a varying number of dimensions as often found in
real-world applications. This paper presents an incremental DR solution. We
enhance an existing incremental PCA method in several ways to ensure its
usability for visualizing streaming multidimensional data. First, we use
geometric transformation and animation methods to help preserve a viewer's
mental map when visualizing the incremental results. Second, to handle data
dimension variants, we use an optimization method to estimate the projected
data positions, and also convey the resulting uncertainty in the visualization.
We demonstrate the effectiveness of our design with two case studies using
real-world datasets.
