# Adaptive Singular Value Thresholding

## TL;DR

This paper introduces an adaptive thresholding method for low-rank matrix recovery that dynamically adjusts the threshold during iterations, leading to improved performance over traditional fixed-threshold approaches.

## Contribution

The novel adaptive singular value thresholding (ASVT) method adjusts thresholds iteratively, enhancing low-rank recovery under affine constraints compared to existing fixed-threshold methods.

## Key findings

- Better recovery performance demonstrated in simulations
- Adaptive threshold decreases during iterations
- Outperforms fixed-threshold methods

## Abstract

In this paper, we propose an Adaptive Singular Value Thresholding (ASVT) for low rank recovery under affine constraints. Unlike previous iterative methods that the threshold level is independent of the iteration number, in our proposed method, the threshold in adaptively decreases during iterations. The simulation results reveal that we get better performance with this thresholding strategy.