报告题目：Tensor-based method for the low-rank approximation of data streams

报告人：肖传福 湘潭大学

报告时间：2026年8月3日(星期一) 9:30-10:30

报告地点：伍卓群楼三楼研讨室5

校内联系方式：邹婷婷 [email protected]

报告摘要：

  Low-rank approximation in data streams is a fundamental and significant task in computing science, machine learning and statistics. Multiple streaming algorithms have emerged over the years and most of them are inspired by randomized algorithms, more specifically, sketching methods. However, many algorithms are not able to leverage information from data streams and consequently suffer from low accuracy. Existing data-driven methods improve accuracy but the training cost is expensive in practice. In this paper, from a subspace perspective, we propose a tensor-based sketching method for low-rank approximation of data streams. The proposed algorithm fully exploits the structure of data streams and obtains quasi-optimal sketching matrices by performing tensor decomposition on training data. A series of experiments are carried out and show that the proposed tensor-based method can be more accurate and much faster than the previous work.

报告人简介：

  肖传福，湘潭大学数学与计算科学香蕉视频 讲师，主要研究兴趣包括矩阵/张量计算及其应用、模型约简、并行计算等，致力于发展“张量表示+机器学习”工具并将其应用于解决科学计算、数据科学等领域中的实际问题；在国际计算数学期刊和人工智能会议上发表论文十余篇，其中包括Math Comp，SIAM系列期刊（SISC、SIMAX）, Journal of Scientific Computing (JSC), International Conference on Learning Representations（ICLR）等。