Low-rank compression

Low-rank compression is a matrix approximation technique aimed at reducing data storage and computational costs by lowering the rank of a matrix while retaining critical information. This method achieves efficient data compression and processing by decomposing high-dimensional matrices into the product of low-rank matrices. In large-scale datasets and high-dimensional feature spaces, low-rank compression can significantly enhance the runtime efficiency and scalability of algorithms, and it is widely applied in machine learning, data mining, computer vision, and other fields.