Calibrating Statistical Pattern Recognition Techniques Based on Large-Dimensional Random Matrix Theory

Project: Monitored by Research Administration

Project Details

Grant Program

Faculty Development Competitive Research Grant Program 2021-2023

Project Description

Our goal in this project is to calibrate some fundamental statistical techniques in pattern recognition for small-sample using the theory of random matrices of large dimension, which is commonly referred to as random matrix theory. The underlying hypothesis of this project is that calibrating these methods using random matrix theory will improve their performance in small-sample.
StatusNot started

Keywords

  • Statistical Pattern Recognition
  • Machine Learning
  • Random Matrix Theory

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