 Regularization by spectral filtering

Regularization by spectral filtering is a technique used in fields such as statistics, machine learning, and signal processing to address issues of overfitting and to improve the stability of the solutions to inverse problems. The basic concept revolves around separating the signal (or data) of interest from noise by manipulating its spectral content—typically in the frequency domain. ### Key Concepts: 1. **Spectral Domain**: Spectral filtering involves transforming data into the frequency domain, usually via techniques like the Fourier Transform.