Eigen Portfolio

Eigen-based methods leverage the spectral properties of matrices (eigenvalues and eigenvectors) to solve diverse problems across numerous scientific domains. Current research focuses on applying these techniques to improve efficiency and interpretability in machine learning models (e.g., neural networks, LLMs), enhance signal processing and image analysis (e.g., hyperspectral image super-resolution, 3D model fairing), and optimize complex systems (e.g., portfolio management, robotic odometry). This approach offers significant potential for improving computational efficiency, enhancing model performance, and gaining deeper insights into the underlying structure of data and systems.