An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data
This work addresses the challenge of real-time visualization for streaming data in applications like monitoring systems, but it is incremental as it builds on prior incremental PCA methods.
The paper tackles the problem of visualizing streaming multidimensional data with varying dimensions by enhancing an existing incremental PCA method, using geometric transformations and optimization to preserve mental maps and handle dimension variants, and demonstrates effectiveness through two case studies with real-world datasets.
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.