An Efficient SVD Shrinkage for Rank Estimation

Matrix rank estimation is a classical problem with many applications in statistical signal processing. In this letter, a logistic function based thresholding of the singular values is proposed for the rank estimation purpose. Parameters of the proposed shrinkage function are tuned using Stein´s unbiased risk estimator. The proposed method is shown to outperform the state-of-the-art methods in terms of rank estimation accuracy. Further, it is also noted to result in a better denoising performance.