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Title: Visualizing Topological Importance: A Class-Driven Approach
This paper presents the first approach to visualize the importance of topological features that define classes of data. Topological features, with their ability to abstract the fundamental structure of complex data, are an integral component of visualization and analysis pipelines. Although not all topological features present in data are of equal importance. To date, the default definition of feature importance is often assumed and fixed. This work shows how proven explainable deep learning approaches can be adapted for use in topological classification. In doing so, it provides the first technique that illuminates what topological structures are important in each dataset in regards to their class label. In particular, the approach uses a learned metric classifier with a density estimator of the points of a persistence diagram as input. This metric learns how to reweigh this density such that classification accuracy is high. By extracting this weight, an importance field on persistent point density can be created. This provides an intuitive representation of persistence point importance that can be used to drive new visualizations. This work provides two examples: Visualization on each diagram directly and, in the case of sublevel set filtrations on images, directly on the images themselves. This work highlights  more » « less
Award ID(s):
2136744
NSF-PAR ID:
10474749
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IEEE Workshop on Topological Data Analysis and Visualization
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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