Signal Subspace

Signal subspace methods aim to identify lower-dimensional representations of high-dimensional data, focusing on the essential information within a data set while discarding noise or irrelevant features. Current research explores applications across diverse fields, including continual learning (using subspace partitioning for improved knowledge retention), multi-task model fusion (leveraging shared subspaces to resolve conflicts between models), and anomaly detection (using subspace differences to identify deviations from normal patterns). These techniques offer improved efficiency and robustness in various applications, from industrial process monitoring to image processing and distributed learning, by reducing computational complexity and enhancing model generalization.