Optimal smooth approximation for Quantile Matrix Factorization

Matrix Factorization (MF) is essential to many estimation tasks. Most existing matrix factorization methods focus on least squares matrix factorization (LSMF), which aims to minimize a smooth L2 loss between observations and their dependent matrix measurement variables. In reality, however, L1 loss...

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Bibliographic Details

Main Authors:	Peng Liu, Yi Liu, Rui Zhu, Linglong Kong, Bei Jiang, Di Niu
Format:	Default Conference proceeding
Published:	2023
Subjects:	Quantile Matrix Factorization (QMF)
Online Access:	https://hdl.handle.net/2134/27157293.v1
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