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Found 4 papers, 2 papers with code
Perturbation Robust Representations of Topological Persistence Diagrams
However, persistence diagrams are multi-sets of points and hence it is not straightforward to fuse them with features used for contemporary machine learning tools like deep-nets.
A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams
This paper concerns itself with one popular topological feature, which is the number of $d-$dimensional holes in the dataset, also known as the Betti$-d$ number.
Persistent Homology of Attractors For Action Recognition
In this paper, we propose a novel framework for dynamical analysis of human actions from 3D motion capture data using topological data analysis.
Shape Distributions of Nonlinear Dynamical Systems for Video-based Inference
Our experimental analyses on these models also indicate that the proposed framework show stability for different time-series lengths, which is useful when the available number of samples are small/variable.
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