How Eigendecomposition Powers Image Compression and Signal Clarity
Eigendecomposition, a cornerstone of linear algebra, reveals hidden patterns in data matrices by transforming them into sets of orthogonal eigenvectors and associated eigenvalues. This process enables powerful dimensionality reduction—extracting dominant features while discarding redundancy. In signal science, particularly in images, this mathematical insight turns complex pixel data into compact, meaningful representations that drive efficient compression […]