Recovering Quantized Data with Missing Information Using Bilinear Factorization and Augmented Lagrangian Method
Year: 2018
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Esmaeili, Ashkan, Behdin, Kayhan, Al-E-Mohammad, Sina, Marvasti, Farokh
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2895091835