Abstract:
Topological Data Analysis (TDA) provides tools to describe the shape of data, but integrating topological features into deep learning pipelines remains challenging, especially when preserving local geometric structure rather than summarizing it globally.
We propose a persistence-based data augmentation framework that encodes local gradient flow regions and their hierarchical evolution using the Morse–Smale complex.
This representation, compatible with both convolutional and graph neural networks, retains spatially localized topological information across multiple scales.
Importantly, the augmentation procedure itself is efficient, with computational complexity
Scheduled for: 2025-08-12 02:30 PM: Computing Session Talk #3.1 in HUMB 142
Status: Accepted
Collection: Topology and Computing
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