Orthogonality

Orthogonality is a highly important concept in linear algebra for independence, separability, and numerical stability. If two vectors are orthogonal, their dot product is zero, meaning they do not linearly interfere with one another. In data science and machine learning, orthogonality plays a major role in feature separation, basis transformations, projections, and methods such as SVD. Especially in high-dimensional spaces, orthogonal structures help represent information in a cleaner and more interpretable way. For that reason, orthogonality is not only geometric, but also central to modeling quality.