Abstract:
Low-rank minimization recovers a minimum-rank matrix under linear system constraint. Recommender systems, signal processing, and video denoising use it. Nuclear norm minimization dominates. However, it ignores target matrix singular values. Nonconvex low-rank regularizers solve this problem. Existing methods are inefficient and inaccurate. This article proposes a flexible model with a novel nonconvex regularizer to solve such issues. It promotes low rankness and solves faster and more accurately. It transforms the low-rank problem into the optimization problem under the rank restricted isometry property (rank-RIP) condition. Nesterov’s rule and inexact proximal strategies are used to create a novel algorithm that solves this problem at a convergence rate of O(1/K), where K is the iterate count. Using the Kurdyka-ojasiewicz (KL) inequality, the asymptotic convergence rate is rigorously analyzed. The optimization model is also applied to matrix completion, robust principal component analysis (RPCA), and tensor completion. Extensive empirical studies on synthetic data analysis, image recovery, personalized recommendation, and background subtraction show that the proposed model outperforms state-of-the-art models in accuracy and efficiency.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
Did you like this final year project?
To download this project Code with thesis report and project training... Click Here
