If a matrix undergoes singular value decomposition (SVD) resulting in clearer patterns in data for machine learning applications, what underlying property of the matrix is primarily responsible for this effect?

The effect comes from the matrix’s ability to be described mainly by a few large singular values, which means it has a low‑rank or “compressible” structure. In an SVD, the singular values tell how much each orthogonal direction contributes to the data; when most of the energy is in the first few values, the matrix can be approximated well by a small number of components. This low‑rank property lets machine‑learning algorithms focus on the strongest patterns and ignore noise. For example, a 100×100 image matrix whose first ten singular values capture 95 % of the total energy can be reduced to a 10‑dimensional representation that still preserves the image’s main features.