تقرير
A Wiener process perspective on local intrinsic dimension estimation methods
| العنوان: | A Wiener process perspective on local intrinsic dimension estimation methods |
|---|---|
| المؤلفون: | Tempczyk, Piotr, Garncarek, Łukasz, Filipiak, Dominik, Kurpisz, Adam |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
| الوصف: | Local intrinsic dimension (LID) estimation methods have received a lot of attention in recent years thanks to the progress in deep neural networks and generative modeling. In opposition to old non-parametric methods, new methods use generative models to approximate diffused dataset density and scale the methods to high-dimensional datasets like images. In this paper, we investigate the recent state-of-the-art parametric LID estimation methods from the perspective of the Wiener process. We explore how these methods behave when their assumptions are not met. We give an extended mathematical description of those methods and their error as a function of the probability density of the data. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.17125 |
| رقم الأكسشن: | edsarx.2406.17125 |
| قاعدة البيانات: | arXiv |
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