Summary of Unsupervised Assessment Of Landscape Shifts Based on Persistent Entropy and Topological Preservation, by Sebastian Basterrech
Unsupervised Assessment of Landscape Shifts Based on Persistent Entropy and Topological Preservation
by Sebastian Basterrech
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes a novel framework for monitoring concept drift in multi-dimensional data streams. In a Continual Learning (CL) context, concept drift typically refers to changes in data distribution. However, the authors consider an alternative perspective where the concept drift also involves significant topological changes in the data stream. The proposed approach is based on persistent entropy and topology-preserving projections, and it operates in both unsupervised and supervised environments. To demonstrate its effectiveness, the framework is tested across three scenarios using MNIST samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new way to track changes in big datasets that come from different places or times. This is important because when we learn from these kinds of data streams, things can get out of sync if the data keeps changing. The team’s method looks at both what’s happening with the data and how it’s connected, which helps detect big shifts. They tested their approach on some handwritten digit samples and found that it works well. |
Keywords
» Artificial intelligence » Continual learning » Supervised » Unsupervised
