Noise Sensitivity in Sparse Random Matrix's Top Eigenvector Analysis

Understanding the noise sensitivity of the top eigenvector in sparse random matrices through resampling procedures, exploring the threshold phenomenon and related works. Results highlight the impact of noise on the eigenvector's stability and reliability in statistical analysis.

• Random Matrix
• Eigenvector
• Noise Sensitivity
• Threshold Phenomenon
• Resampling

aitana Follow

Uploaded on Aug 23, 2024 | 0 Views

Download Presentation

Please find below an Image/Link to download the presentation.

The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.

You are allowed to download the files provided on this website for personal or commercial use, subject to the condition that they are used lawfully. All files are the property of their respective owners.

The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author.

Presentation Transcript

1. Noise sensitivity for the top eigenvector of a sparse random matrix

2. Top eigenvector of sparse random matrices

3. Resampling procedure

4. Result: threshold phenomenon

5. Result: threshold phenomenon Thank you and see you soon!

7. Result: threshold phenomenon Related works