Subspace learning is a dimensionality reduction technique used in machine learning and pattern recognition to find a compact representation of data by identifying and focusing on the most relevant features. This approach aims to improve the efficiency and effectiveness of various algorithms by working in a transformed, lower-dimensional space that best preserves significant data characteristics.