Transductive Matrix Completion with Calibration for Multi-Task Learning

Autor:	Wang, Hengfang, Zhang, Yasi, Mao, Xiaojun, Wang, Zhonglei
Rok vydání:	2023
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	Multi-task learning has attracted much attention due to growing multi-purpose research with multiple related data sources. Moreover, transduction with matrix completion is a useful method in multi-label learning. In this paper, we propose a transductive matrix completion algorithm that incorporates a calibration constraint for the features under the multi-task learning framework. The proposed algorithm recovers the incomplete feature matrix and target matrix simultaneously. Fortunately, the calibration information improves the completion results. In particular, we provide a statistical guarantee for the proposed algorithm, and the theoretical improvement induced by calibration information is also studied. Moreover, the proposed algorithm enjoys a sub-linear convergence rate. Several synthetic data experiments are conducted, which show the proposed algorithm out-performs other existing methods, especially when the target matrix is associated with the feature matrix in a nonlinear way. Comment: Accepted by IEEE ICASSP 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.09834 Zobrazit plný text záznamu View this record from Arxiv