Which matrix decomposition technique is best suited for reducing dimensionality in large datasets?

The most common choice for reducing dimensionality in large datasets is Principal Component Analysis, which uses Singular Value Decomposition under the hood. PCA finds new axes that capture the most variation in the data, and it keeps only the first few axes that explain most of the variance. Because the algorithm only keeps a handful of components, the data’s size shrinks dramatically while still retaining the important patterns. For example, a 10,000‑by‑1,000 matrix of image pixels can be compressed to just 50 components, cutting the storage and computation time by a large factor. This makes PCA with SVD the go‑to technique when you need to work efficiently with huge tables.