License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ITCS.2022.46
URN: urn:nbn:de:0030-drops-156422
URL: https://drops.dagstuhl.de/opus/volltexte/2022/15642/
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Chen, Sitan ; Song, Zhao ; Tao, Runzhou ; Zhang, Ruizhe

Symmetric Sparse Boolean Matrix Factorization and Applications

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LIPIcs-ITCS-2022-46.pdf (0.9 MB)


Abstract

In this work, we study a variant of nonnegative matrix factorization where we wish to find a symmetric factorization of a given input matrix into a sparse, Boolean matrix. Formally speaking, given {𝐌} ∈ {ℤ}^{m× m}, we want to find {𝐖} ∈ {0,1}^{m× r} such that ‖ {𝐌} - {𝐖} {𝐖}^⊤ ‖₀ is minimized among all {𝐖} for which each row is k-sparse. This question turns out to be closely related to a number of questions like recovering a hypergraph from its line graph, as well as reconstruction attacks for private neural network training.
As this problem is hard in the worst-case, we study a natural average-case variant that arises in the context of these reconstruction attacks: {𝐌} = {𝐖} {𝐖}^{⊤} for {𝐖} a random Boolean matrix with k-sparse rows, and the goal is to recover {𝐖} up to column permutation. Equivalently, this can be thought of as recovering a uniformly random k-uniform hypergraph from its line graph.
Our main result is a polynomial-time algorithm for this problem based on bootstrapping higher-order information about {𝐖} and then decomposing an appropriate tensor. The key ingredient in our analysis, which may be of independent interest, is to show that such a matrix {𝐖} has full column rank with high probability as soon as m = ΊĖƒ(r), which we do using tools from Littlewood-Offord theory and estimates for binary Krawtchouk polynomials.

BibTeX - Entry

@InProceedings{chen_et_al:LIPIcs.ITCS.2022.46,
  author =	{Chen, Sitan and Song, Zhao and Tao, Runzhou and Zhang, Ruizhe},
  title =	{{Symmetric Sparse Boolean Matrix Factorization and Applications}},
  booktitle =	{13th Innovations in Theoretical Computer Science Conference (ITCS 2022)},
  pages =	{46:1--46:25},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-217-4},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{215},
  editor =	{Braverman, Mark},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/15642},
  URN =		{urn:nbn:de:0030-drops-156422},
  doi =		{10.4230/LIPIcs.ITCS.2022.46},
  annote =	{Keywords: Matrix factorization, tensors, random matrices, average-case complexity}
}

Keywords: Matrix factorization, tensors, random matrices, average-case complexity
Collection: 13th Innovations in Theoretical Computer Science Conference (ITCS 2022)
Issue Date: 2022
Date of publication: 25.01.2022


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