Scalable Affinity Propagation for Massive Datasets
Authors: Hiroaki Shiokawa9639-9646
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Evaluation We experimentally evaluated the effectiveness of Scale AP by comparing it with the following AP algorithms. [...] Datasets: We used three real-world datasets summarized in Table 1. [...] Figure 1 shows the running time on the real-world datasets, where DNF indicates that the runtime exceeded 24 hours. |
| Researcher Affiliation | Academia | Hiroaki Shiokawa Center for Computational Sciences, University of Tsukuba, Japan shiokawa@cs.tsukuba.ac.jp |
| Pseudocode | Yes | Algorithm 1 Proposed method: Scale AP |
| Open Source Code | No | The paper does not provide any concrete statement or link regarding the availability of its source code. |
| Open Datasets | Yes | Datasets: We used three real-world datasets summarized in Table 1. All the datasets are published by UCI Machine Learning Repository (Dua and Graff 2017). |
| Dataset Splits | No | The paper mentions using real-world datasets but does not explicitly provide training, validation, or test dataset splits. |
| Hardware Specification | Yes | All experiments were conducted on a server with Intel Xeon CPU 2.60 GHz and 768 Gi B RAM. |
| Software Dependencies | No | The paper discusses various algorithms and methods but does not provide specific version numbers for software dependencies or libraries used in their implementation. |
| Experiment Setup | Yes | The experiments used the negative Euclidean distance as the similarity between data objects. In accordance with (Frey and Dueck 2007), we set the preference to both the median and minimum of the similarities, λ = 0.5, and the maximum number of iterations to T = 1, 000. |