Traditional linear models in machine learning, such as logistic regression, struggle to grasp the complex characteristics of data in very high dimensions. One type of manifold, the Symmetric Positive Definite (SPD) matrices improve the output quality of logistic regression by enhancing feature representation in a lower-dimensional space.