Semi-supervised feature selection with exploiting shared information among multiple tasks

Given several related tasks, multi-task feature selection determines the importance of features by mining the correlations between them. There have already many efforts been made on the supervised multi-task feature selection. However, in real-world applications, it's noticeably time-consuming and unpractical to collect sufficient labeled training data for each task. In this paper, we propose a novel feature selection algorithm, which integrates the semi-supervised learning and multi-task learning into a joint framework. Both the labeled and unlabeled samples are sufficiently utilized for each task, and the shared information between different tasks is simultaneously explored to facilitate decision making. Since the proposed objective function is non-smooth and difficult to be solved, we also design an efficient iterative algorithm to optimize it. Experimental results on different applications demonstrate the effectiveness of our algorithm.