Principal axes are the directions in which a given system's properties, such as inertia or variance, are maximized or minimized. In the context of linear algebra, they are particularly associated with eigenvalues and eigenvectors, where each principal axis corresponds to an eigenvector and is scaled by its corresponding eigenvalue. Understanding principal axes allows for better insight into the geometric transformations and physical interpretations of matrices, especially in applications like data analysis and engineering.
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