Robust SVD Made Easy: A fast and reliable algorithm for large-scale data analysis

arXiv:2402.09754v1 Announce Type: cross
Abstract: The singular value decomposition (SVD) is a crucial tool in machine learning and statistical data analysis. However, it is highly susceptible to outliers in the data matrix. Existing robust SVD algorithms often sacrifice speed for robustness or fail in the presence of only a few outliers. This study introduces an efficient algorithm, called Spherically Normalized SVD, for robust SVD approximation that is highly insensitive to outliers, computationally scalable, and provides accurate approximations of singular vectors. …

abstract algorithm algorithms analysis arxiv cs.lg data data analysis easy machine machine learning math.st matrix outliers robust robustness scale singular speed statistical stat.ml stat.th svd tool type value