Multilinear subspace learning

Multilinear subspace learning refers to a set of techniques in machine learning and statistics used to analyze and represent data that exists in a multi-dimensional space. While traditional linear subspace methods (like Principal Component Analysis, PCA) focus on linear relationships within data, multilinear methods extend these concepts to accommodate data that can be best modeled in a higher-dimensional space with multiple modes or tensor structures.