A Fast and Accurate Matrix Completion Method Based on QR Decomposition and L -Norm Minimization.

Low-rank matrix completion aims to recover matrices with missing entries and has attracted considerable attention from machine learning researchers. Most of the existing methods, such as weighted nuclear-norm-minimization-based methods and Qatar Riyal (QR)-decomposition-based methods, cannot provide both convergence accuracy and convergence speed. To investigate a fast and accurate completion method, an iterative QR-decomposition-based method is proposed for computing an approximate singular value decomposition.

Digital watermarking robust to geometric distortions.

In this paper, we present two watermarking approaches that are robust to geometric distortions. The first approach is based on image normalization, in which both watermark embedding and extraction are carried out with respect to an image normalized to meet a set of predefined moment criteria. We propose a new normalization procedure, which is invariant to affine transform attacks.