Solving Soft Clustering Ensemble via $k$-Sparse Discrete Wasserstein Barycenter
Authors: Ruizhe Qin, Mengying Li, Hu Ding
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct the experiments to evaluate our proposed algorithms. |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology 2School of Data Science University of Science and Technology of China red46@mail.ustc.edu.cn, limengy@mail.ustc.edu.cn, huding@ustc.edu.cn |
| Pseudocode | Yes | Algorithm 1 (1 + ϵ)-Approximate SCE Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We consider three real datasets: USPS has 11000 data items in R256 with k = 10 [32]; IRIS [22] has 150 data items in R4 with k = 3; CIFAR-10 [38] has 10000 data items in R3072 with k = 10. |
| Dataset Splits | No | The paper describes the datasets used and how clustering solutions were generated (random projections, k-means) but does not specify training, validation, or test splits for the datasets themselves. |
| Hardware Specification | Yes | All the experimental results were obtained on a server equipped with 2.8GHz Intel CPU, 8GB main memory, and Matlab 2019a. |
| Software Dependencies | Yes | All the experimental results were obtained on a server equipped with 2.8GHz Intel CPU, 8GB main memory, and Matlab 2019a. |
| Experiment Setup | Yes | We set m = 1000 (i.e., the number of generated clustering solutions for ensemble)... Similar with [24, 13], we apply random projections to generate the clustering solutions (in each random subspace, we use k-means to cluster the data). ... Our sampling idea of Section 4.2 is incorporated into the alternating minimization Wasserstein barycenter algorithm [62], which is denoted as AM-r with r representing the sample rate... All the results are averaged across 30 trials. |