Tensor-Based Sketching Method for the Low-Rank Approximation of Data Streams.
Authors: Cuiyu Liu, Xiao Chuanfu, Mingshuo Ding, Chao Yang
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. ... In this section, we test our algorithms and compare them to the existing data-driven algorithms for low-rank approximation of data streams. We use three datasets for comparison HSI (Imamoglu et al., 2018), Logo (Indyk et al., 2019) and MRI. |
| Researcher Affiliation | Academia | Cuiyu Liu1, Chuanfu Xiao2 3, Mingshuo Ding1, Chao Yang2 3 1Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China 2School of Mathematical Sciences, Peking University, Beijing, China 3Changsha Institute for Computing and Digital Economy, Changsha, China 2101213203@stu.pku.edu.cn, {chuanfuxiao,dingmingshuo,chao_yang}@pku.edu.cn |
| Pseudocode | Yes | Algorithm 1 The SCW algorithm (Sarlos, 2006; Clarkson & Woodruff, 2009; 2017). ... Algorithm 2 The tensor-based algorithm for low-rank approximation of the data stream D. ... Algorithm 3 The two-sided SCW algorithm. ... Algorithm 4 The two-sided tensor-based algorithm for low-rank approximation of the data stream D. ... Algorithm 5 HOOI algorithm Lathauwer et al. (2000); Kolda & Bader (2009) |
| Open Source Code | No | The paper does not provide a link to open-source code for the described methodology or explicitly state that the code is being released. |
| Open Datasets | Yes | We use three datasets for comparison HSI (Imamoglu et al., 2018), Logo (Indyk et al., 2019) and MRI. ... 1Retrieved from https://github.com/gistairc/HS-SOD. 2Retrieved from http://youtu.be/L5HQo FIa T4I. 3Retrieved from https://brainweb.bic.mni.mcgill.ca/cgi/brainweb2. |
| Dataset Splits | No | The paper mentions training and testing sets, and sample ratios, but does not explicitly state the use or size of a validation set split. |
| Hardware Specification | Yes | Experiments are run on a server equipped with an NVIDIA Tesla V100 card. |
| Software Dependencies | No | The paper mentions "Py Torch" but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | In all experiments, we set the rank r to 10, and the sketching size k = l = 20. |