Recovering Quantized Data with Missing Information Using Bilinear Factorization and Augmented Lagrangian Method
This work addresses data recovery challenges in scenarios with quantization and missing information, though it appears incremental as it builds on existing factorization and optimization techniques.
The paper tackles the problem of recovering quantized matrices with missing data by proposing a regularized convex cost function optimized via bilinear factorization and the Augmented Lagrangian Method, achieving superior accuracy and robustness in computational complexity compared to state-of-the-art methods.
In this paper, we propose a novel approach in order to recover a quantized matrix with missing information. We propose a regularized convex cost function composed of a log-likelihood term and a Trace norm term. The Bi-factorization approach and the Augmented Lagrangian Method (ALM) are applied to find the global minimizer of the cost function in order to recover the genuine data. We provide mathematical convergence analysis for our proposed algorithm. In the Numerical Experiments Section, we show the superiority of our method in accuracy and also its robustness in computational complexity compared to the state-of-the-art literature methods.