Discrete Aware Matrix Completion via Convexized $\ell_0$-Norm Approximation

Autor:	Führling, Niclas, Ando, Kengo, de Abreu, Giuseppe Thadeu Freitas, G., David González, Gonsa, Osvaldo
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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 $\ell_0$-norm regularizer, not by replaced with the $\ell_1$-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 $\ell_1$-norm-based discrete-aware matrix completion approach. Comment: Accepted at the IEEE 2024 Asilomar Conference on Signals, Systems, and Computers
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.02101 Zobrazit plný text záznamu View this record from Arxiv