Transductive Matrix Completion with Calibration for Multi-Task Learning
Hengfang Wang, Yasi Zhang, Xiaojun Mao, Zhonglei Wang
Abstract
Multi-task learning has attracted much attention due to growing multi-purpose research with multiple related data sources. More- over, 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 methods, especially when the target matrix is associated with the feature matrix in a nonlinear way.
BibTeX
@inproceedings{icassp2023_transductivematr,
title = {Transductive Matrix Completion with Calibration for Multi-Task Learning},
author = {Hengfang Wang and Yasi Zhang and Xiaojun Mao and Zhonglei Wang},
booktitle = {ICASSP 2023},
year = {2023}
}