Statistics of Min-max Normalized Eigenvalues in Random Matrices
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917151772770304 |
|---|---|
| author | Nakada, Hyakka Tanaka, Shu |
| author_facet | Nakada, Hyakka Tanaka, Shu |
| contents | Random matrix theory has played an important role in various areas of pure mathematics, mathematical physics, and machine learning. From a practical perspective of data science, input data are usually normalized prior to processing. Thus, this study investigates the statistical properties of min-max normalized eigenvalues in random matrices. Previously, the effective distribution for such normalized eigenvalues has been proposed. In this study, we apply it to evaluate a scaling law of the cumulative distribution. Furthermore, we derive the residual error that arises during matrix factorization of random matrices. We conducted numerical experiments to verify these theoretical predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15427 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistics of Min-max Normalized Eigenvalues in Random Matrices Nakada, Hyakka Tanaka, Shu Machine Learning Statistical Mechanics Statistics Theory Random matrix theory has played an important role in various areas of pure mathematics, mathematical physics, and machine learning. From a practical perspective of data science, input data are usually normalized prior to processing. Thus, this study investigates the statistical properties of min-max normalized eigenvalues in random matrices. Previously, the effective distribution for such normalized eigenvalues has been proposed. In this study, we apply it to evaluate a scaling law of the cumulative distribution. Furthermore, we derive the residual error that arises during matrix factorization of random matrices. We conducted numerical experiments to verify these theoretical predictions. |
| title | Statistics of Min-max Normalized Eigenvalues in Random Matrices |
| topic | Machine Learning Statistical Mechanics Statistics Theory |
| url | https://arxiv.org/abs/2512.15427 |