Unlocking Data Insights Through Activity Vector Analysis: A Comprehensive Guide
Activity vector analysis employs mathematical vectors to represent multidimensional data patterns. It decomposes activity vectors into components along orthogonal basis vectors (eigenvectors), revealing underlying patterns and relationships. Eigenvalues associated with eigenvectors indicate data variability, aiding dimensionality reduction techniques like PCA. SVD generalizes PCA by extending its applicability beyond orthogonal data. Activity vector analysis has wide-ranging…