A Fast Adaptive Randomized PCA Algorithm
Authors: Xu Feng, Wenjian Yu
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that far PCA is much faster than the baseline methods (rand QB EI, rand UBV and svds) in practical setting of multi-thread computing, while producing nearly optimal results of adpative PCA. |
| Researcher Affiliation | Academia | Xu Feng and Wenjian Yu Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, China fx17@mails.tsinghua.edu.cn, yu-wj@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1 Basic randomized PCA with power iteration ... Algorithm 5 Fast adaptive randomized PCA (far PCA) |
| Open Source Code | Yes | The codes of far PCA are shared on Git Hub (https://github.com/XuFengthucs/farPCA). |
| Open Datasets | Yes | Four real-world matrices are considered for testing. ... The other three are sparse matrices: an 82, 168 82, 168 social network matrix from SNAP [Leskovec and Krevl, 2014] with 948,464 nonzero elements, a 138, 493 26, 744 matrix from Movielens dataset [Harper and Konstan, 2016] named Movielens-20m with 20,000,263 nonzero elements, and a 270, 896 45, 115 matrix from Movielens dataset named Movielens with 26,024,289 nonzero elements, which is larger than Movielens-20m. |
| Dataset Splits | No | The paper refers to 'real-world test cases' but does not specify explicit training, validation, or test dataset splits (e.g., percentages, sample counts, or predefined splits with citations). |
| Hardware Specification | Yes | All experiments are carried out on a Ubuntu server with two 8-core Intel Xeon CPU (at 2.10 GHz) and 512 GB RAM. |
| Software Dependencies | Yes | The proposed algorithms are implemented both in Matlab and in C with MKL2 and Open MP directives for multi-thread parallel computing. ... svds in Matlab 2020b is used for computing the accurate results. |
| Experiment Setup | Yes | The error tolerance is set ε = 0.1 A F for the case Image, and ε = 0.5 A F for the rest cases. The block parameter b is set to min(m, n)/100. ... we vary power parameter p while keeping b = 20 and k = 200 |