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What is Fast Monte-Carlo Algorithms For Finding Low-Rank Approximations about?

This paper presents a randomized algorithm for approximating a large matrix A by a low-rank matrix D* of specified rank k, significantly speeding up the computation compared to traditional Singular Value Decomposition (SVD). The algorithm operates in polynomial time relative to k, 1/ε, and log(1/δ), and is independent of the dimensions m and n of the matrix, making it suitable for large datasets. The approach relies on sampling matrix entries according to a natural probability distribution, allowing for eff

Author

Niraj Venkat

Language

EN