Subspace Identification

Subspace identification focuses on extracting relevant information from high-dimensional data by identifying lower-dimensional subspaces that capture essential system characteristics. Current research emphasizes robust and efficient algorithms, including Bayesian and probabilistic approaches, to handle noisy or incomplete data, particularly in applications like operational modal analysis and federated learning. These advancements improve the accuracy and reliability of model estimation across diverse fields, from structural health monitoring to anomaly detection in complex networks. The development of stable and computationally tractable methods is a key focus, enhancing the applicability of subspace identification to real-world problems.