  In this paper, we propose a novel semi-supervised feature selection framework
by mining correlations among multiple tasks and apply it to different
multimedia applications. Instead of independently computing the importance of
features for each task, our algorithm leverages shared knowledge from multiple
related tasks, thus, improving the performance of feature selection. Note that
we build our algorithm on assumption that different tasks share common
structures. The proposed algorithm selects features in a batch mode, by which
the correlations between different features are taken into consideration.
Besides, considering the fact that labeling a large amount of training data in
real world is both time-consuming and tedious, we adopt manifold learning which
exploits both labeled and unlabeled training data for feature space analysis.
Since the objective function is non-smooth and difficult to solve, we propose
an iterative algorithm with fast convergence. Extensive experiments on
different applications demonstrate that our algorithm outperforms other
state-of-the-art feature selection algorithms.
