Difference Subspace

Difference subspace analysis focuses on quantifying and understanding the geometric discrepancies between multiple subspaces, often within high-dimensional data. Current research explores extensions to higher-order differences (e.g., second-order difference subspaces), applications in diverse fields like time-series analysis, and the development of algorithms for efficient subspace identification and manipulation within models such as Low-Rank Adaptation (LoRA) and State Space Models. This work is significant for improving anomaly detection, enhancing machine learning model robustness (including large language models), and enabling more efficient analysis of complex datasets across various scientific disciplines.