Summary of Mnist-nd: a Set Of Naturalistic Datasets to Benchmark Clustering Across Dimensions, by Polina Turishcheva et al.
MNIST-Nd: a set of naturalistic datasets to benchmark clustering across dimensions
by Polina Turishcheva, Laura Hansel, Martin Ritzert, Marissa A. Weis, Alexander S. Ecker
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: The paper addresses the challenge of clustering high-dimensional datasets, which are increasingly prevalent in various scientific fields, including biology. To better understand how dimensionality affects clustering performance, the authors propose MNIST-Nd, a set of synthetic datasets generated by training mixture variational autoencoders on MNIST with varying latent dimensions (2-64). This dataset allows for exploring the impact of dimensionality on clustering algorithms. Preliminary benchmarking on MNIST-Nd shows that Leiden is the most robust algorithm for increasing dimensionality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Scientists are dealing with huge amounts of data from different fields, including biology. To understand how these datasets are organized, people often use a technique called clustering. But this method gets worse as the number of features increases. The authors created some fake datasets that mimic real-world datasets and varied their size to see how it affects clustering performance. They found that a specific algorithm, Leiden, works better than others when dealing with larger datasets. |
Keywords
» Artificial intelligence » Clustering
