Subspace Methods

Subspace methods are techniques used in data analysis and signal processing to identify and extract important patterns from complex data by focusing on a smaller, more manageable "subspace" within the full data space. This involves projecting high-dimensional data onto a lower-dimensional space that captures the essential information, making tasks like noise reduction, feature extraction, or system identification more efficient and accurate. Think of it as finding the underlying structure or "core" of the data, simplifying analysis while preserving meaningful information.