Summary of Stiefelgen: a Simple, Model Agnostic Approach For Time Series Data Augmentation Over Riemannian Manifolds, by Prasad Cheema et al.
StiefelGen: A Simple, Model Agnostic Approach for Time Series Data Augmentation over Riemannian Manifolds
by Prasad Cheema, Mahito Sugiyama
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed methodology tackles limitations in existing data augmentation approaches for time series data, which include a lack of access to robust physical models, difficulties in adding noise without making assumptions, and challenges in sourcing large representative datasets. The method leverages matrix differential geometry on the Stiefel manifold to smoothly perturb time series signals, providing a novel way to augment data. This approach is showcased through several potential use cases that take advantage of the unique properties of this underlying manifold. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data augmentation for time series data is challenging due to limitations in existing methods. The proposed method uses matrix differential geometry on the Stiefel manifold to smoothly perturb time series signals, making it a useful tool for augmenting data. This method can be applied to various use cases and takes advantage of the unique properties of this underlying manifold. |
Keywords
* Artificial intelligence * Data augmentation * Time series
