تقرير
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
| العنوان: | LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood |
|---|---|
| المؤلفون: | Tempczyk, Piotr, Michaluk, Rafał, Garncarek, Łukasz, Spurek, Przemysław, Tabor, Jacek, Goliński, Adam |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
| الوصف: | Most of the existing methods for estimating the local intrinsic dimension of a data distribution do not scale well to high-dimensional data. Many of them rely on a non-parametric nearest neighbors approach which suffers from the curse of dimensionality. We attempt to address that challenge by proposing a novel approach to the problem: Local Intrinsic Dimension estimation using approximate Likelihood (LIDL). Our method relies on an arbitrary density estimation method as its subroutine and hence tries to sidestep the dimensionality challenge by making use of the recent progress in parametric neural methods for likelihood estimation. We carefully investigate the empirical properties of the proposed method, compare them with our theoretical predictions, and show that LIDL yields competitive results on the standard benchmarks for this problem and that it scales to thousands of dimensions. What is more, we anticipate this approach to improve further with the continuing advances in the density estimation literature. Comment: ICML 2022 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2206.14882 |
| رقم الأكسشن: | edsarx.2206.14882 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |