Multi-view partial label machine

Liang Zhao, Yanshan Xiao, Bo Liu, Zhifeng Hao

Abstract: In partial label learning (PLL), each instance is associated with a set of candidate labels, among which there is only ground-truth label. PLL aims to identify the ground-truth label out of these candidate labels. Most of the existing PLL approaches are proposed to handle the single-view PLL problem, and the multi-view PLL problem has not been addressed. In this paper, we propose a novel multi-view paunknown.