ORIGINAL RESEARCH article

Front. Appl. Math. Stat.

Sec. Optimization

Volume 11 - 2025 | doi: 10.3389/fams.2025.1589033

This article is part of the Research TopicLarge Tensor Analysis and ApplicationsView all 4 articles

A Sparse Tensor Generator with Efficient Feature Extraction

Provisionally accepted
Tugba  TorunTugba TorunAmeer  TaweelAmeer TaweelDidem  UnatDidem Unat*
  • Koç University, Istanbul, Türkiye

The final, formatted version of the article will be published soon.

Sparse tensor operations are increasingly important in diverse applications such as social networks, deep learning, diagnosis, crime, and review analysis. However, a major obstacle in sparse tensor research is the lack of large-scale sparse tensor datasets. Another challenge lies in analyzing sparse tensor features, which are essential not only for understanding the nonzero pattern but also for selecting the most suitable storage format, decomposition algorithm, and reordering methods. However, due to the large size of real-world tensors, even extracting these features can be computationally expensive without careful optimization. To address these limitations, we have developed a smart sparse tensor generator that replicates key characteristics of real sparse tensors. Additionally, we propose efficient methods for extracting a comprehensive set of sparse tensor features. The effectiveness of our generator is validated through the quality of extracted features and the performance of decomposition on the generated tensors. Both the sparse tensor feature extractor and the tensor generator are open source with all the artifacts available at https://github.com/sparcityeu/FeaTensor and https://github.com/ sparcityeu/GenTensor, respectively.

Keywords: Sparse tensor, tensor generators, feature extraction, Synthetic data generation, shared memory parallelism

Received: 06 Mar 2025; Accepted: 30 Jun 2025.

Copyright: © 2025 Torun, Taweel and Unat. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Didem Unat, Koç University, Istanbul, Türkiye

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.