Stefania Ebli, Celia Hacker, Kelly Maggs

This repository contains the code used in the paper [Signal Compression and Reconstruction on Complexes via Discrete Morse Theory].

At the intersection of Topological Data Analysis (TDA) and machine learning, the field of cellular signal processing has advanced rapidly in recent years. In this context, each signal is processed using the combinatorial Laplacian, and the resultant Hodge decomposition. Meanwhile, discrete Morse theory has been widely used to speed up computations by reducing the size of complexes while preserving their global topological properties. We provide an approach to signal compression and reconstruction on complexes that leverages the tools of discrete Morse theory. The main goal is to collapse and reconstruct a complex together with a set of signals on its cells while preserving as much as possible the global topological structure of both the complex and the signal. We study how the signal changes under particular types of discrete Morse theoretic collapses, showing its reconstruction error is trivial on specific components of the Hodge decomposition. Furthermore, we provide an algorithm to compute collapses with minimal reconstruction error.

[Signal Compression and Reconstruction on Complexes via Discrete Morse Theory]:

• Paper: [arXiv:][paper]

[paper]:

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1. Clone this repository.

   git clone https://github.com/stefaniaebli/signal-DMTheory.git
   cd cell-signal-processing-DMTheory

2. Create the environment.

   CONDA_CHANNEL_PRIORITY=flexible conda env create -f environment.yml
   conda activate sdmt

• dmtsignal.py:
  • stores cell complexes
  • computes boundaries
  • computes laplacians
  • computes collapsed complexes and their respective boundaries
  • computes compressed and reconstructed signal
  • computes optimal up-matchings
  • computes optimal down-matchings
  • computes random sequences of collapses
• dmtvisual.py:
  • visualizes nodes, edges and traingles in simplicial complexes
  • visualizes collapsed complexes and the compressed and reconstructed signal

• up-collapses.ipynb: exlopres signal compression and reconstruction with up-collapses and provides examplesof optimal up-matching algorithm.
• down-collapses.ipynb: exlopres signal compression and reconstruction with down-collapses and provides example sof optimal down-matching algorithm.

The content of this repository is released under the terms of the MIT license. Please cite our paper if you use it.

@inproceedings{signal-dmt,
  title = {Signal Compression and Reconstruction on Complexes via Discrete Morse Theory},
  author = {Ebli, Stefania and Hacker, Celia and Maggs, Kelly},
  booktitle = {},
  year = {2021},
  archiveprefix = {arXiv},
  eprint = {},
  url = {},
}