On Geometric Alignment in Low Doubling Dimension
Authors: Hu Ding, Mingquan Ye1460-1467
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we test our method on both random and real datasets; the experimental results reveal that running the alignment algorithm on compressed patterns can achieve similar qualities, comparing with the results on the original patterns, but the running times (including the times cost for compression) are substantially lower. |
| Researcher Affiliation | Academia | Hu Ding School of Computer Science and Technology School of Data Science University of Science and Technology of China He Fei, China, 230026 huding@ustc.edu.cn; Mingquan Ye Department of Computer Science and Engineering Michigan State University East Lansing, MI, USA, 48824 yemingqu@msu.edu |
| Pseudocode | Yes | Algorithm 1 Geometric Alignment |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating the availability of its source code. |
| Open Datasets | Yes | For real datasets, we consider the two applications mentioned in Section 1, unsupervised bilingual lexicon induction and PPI network alignment. ... Given by (Zhang et al. 2017), each language has a vocabulary list containing 3000 to 13000 words; we also follow their preprocessing idea to represent all the words by vectors in R50 through the embedding technique (Mikolov, Le, and Sutskever 2013). For the second application, we use the popular benchmark dataset NAPAbench (Sayed Mohammad and Yoon 2012) of PPI networks. |
| Dataset Splits | No | The paper describes the datasets used (randomly generated and real-world linguistic/PPI datasets) but does not provide specific details on how these datasets were split into training, validation, or test sets. |
| Hardware Specification | Yes | All of the experimental results are obtained on a Windows workstation with 2.4GHz Intel Xeon CPU and 32GB DDR4 Memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as programming language versions or library version numbers, used for the experiments. |
| Experiment Setup | Yes | We set the iterative approach (Cohen and Guibas 1999) to terminate when the change of the objective value is less than 10 3. We set k = γ max{n1, n2} where γ {1/50, 1/40, 1/30, 1/20, 1/10, 1}. We set the standard variance of the Gaussian noise to be η , where is the maximum diameter of the point sets and η is from 0.5 10 2 to 2.5 10 2. |