Discrete Aware Matrix Completion via Convexized $\ell_0$-Norm Approximation
摘要
We consider a novel algorithm, for the completion of partially observed low-rank matrices in a structured setting where each entry can be chosen from a finite discrete alphabet set, such as in common recommender systems. The proposed low-rank matrix completion (MC) method is an improved variation of state-of-the-art (SotA) discrete aware matrix completion method which we previously proposed, in which discreteness is enforced by an -norm regularizer, not by replaced with the -norm, but instead approximated by a continuous and differentiable function normalized via fractional programming (FP) under a proximal gradient (PG) framework. Simulation results demonstrate the superior performance of the new method compared to the SotA techniques as well as the earlier -norm-based discrete-aware matrix completion approach.
引用
@article{arxiv.2405.02101,
title = {Discrete Aware Matrix Completion via Convexized $\ell_0$-Norm Approximation},
author = {Niclas Führling and Kengo Ando and Giuseppe Thadeu Freitas de Abreu and David González G. and Osvaldo Gonsa},
journal= {arXiv preprint arXiv:2405.02101},
year = {2024}
}
备注
Accepted at the IEEE 2024 Asilomar Conference on Signals, Systems, and Computers