Multiscale Feature Attribution for Outliers
Abstract
Machine learning techniques can automatically identify outliers in massive datasets, much faster and more reproducible than human inspection ever could. But finding such outliers immediately leads to the question: which features render this input anomalous? We propose a new feature attribution method, Inverse Multiscale Occlusion, that is specifically designed for outliers, for which we have little knowledge of the type of features we want to identify and expect that the model performance is questionable because anomalous test data likely exceed the limits of the training data. We demonstrate our method on outliers detected in galaxy spectra from the Dark Energy Survey Instrument and find its results to be much more interpretable than alternative attribution approaches.
- Publication:
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arXiv e-prints
- Pub Date:
- October 2023
- DOI:
- 10.48550/arXiv.2310.20012
- arXiv:
- arXiv:2310.20012
- Bibcode:
- 2023arXiv231020012S
- Keywords:
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- Computer Science - Machine Learning;
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Computer Science - Artificial Intelligence
- E-Print:
- 6 pages, 2 figures, accepted to NeurIPS 2023 Workshop on Machine Learning and the Physical Sciences. Code available at https://github.com/al-jshen/imo