Summary of Linear-time One-class Classification with Repeated Element-wise Folding, by Jenni Raitoharju
Linear-time One-Class Classification with Repeated Element-wise Folding
by Jenni Raitoharju
First submitted to arxiv on: 21 Aug 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 Repeated Element-wise Folding (REF) method offers a straightforward solution for one-class classification, repeatedly standardizing and applying an element-wise folding operation on the training data. This linear-time approach outperforms more complex algorithms on various benchmark datasets, providing robust default settings that eliminate the need for hyperparameter tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a simple way to categorize things when you only have examples of one type. The method, called Repeated Element-wise Folding (REF), works by taking the training data and repeating an operation that makes it easier to compare new items. This makes it fast and easy to use, and also helps avoid needing to adjust many settings. The results show that REF does just as well or even better than other methods on different datasets. |
Keywords
» Artificial intelligence » Classification » Hyperparameter
