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A Riemannian framework for statistical analysis of topological persistence diagrams

License: MIT License

MATLAB 2.03% HTML 17.66% Makefile 0.77% Java 21.41% M4 13.40% C 15.22% Python 8.91% C++ 20.46% M 0.13%
topology machine-learning persistence-diagrams tda time-series-analysis topological-persistence-diagrams riemannian-framework statistical-analysis

pdsphere's Introduction

##Computing an efficient metric between persistence diagrams

Matlab code to compute an extremely efficient metric between topological persistence diagrams by embedding them on to a Hilbert Sphere. If you use this code, please consider citing the following paper:

Anirudh, R., Venkataraman, V., Ramamurthy, K.N. and Turaga, P., A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams. arXiv preprint arXiv:1605.08912., 2016.

Bibtex:

@InProceedings{AnirudhCVPRW2016,
author={Anirudh, Rushil and Venkataraman, Vinay and Ramamurthy, Karthikeyan Natesan and Turaga, Pavan},
title={A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition Workshops},
month = {June},
year = {2016}
}

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