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.

该论文提出了一种新颖的半监督特征选择框架，通过挖掘多个任务之间的相关性，并将其应用于不同的多媒体应用程序，从而提高特征选择的性能。

通过挖掘多个任务之间的相关性进行半监督特征分析

Semi-supervised Feature Analysis by Mining Correlations among Multiple  Tasks

We analyze the expected cost of a greedy active learning algorithm. Our
analysis extends previous work to a more general setting in which different
queries have different costs. Moreover, queries may have more than two possible
responses and the distribution over hypotheses may be non uniform. Specific
applications include active learning with label costs, active learning for
multiclass and partial label queries, and batch mode active learning. We also
discuss an approximate version of interest when there are very many queries.

对一种贪心主动学习算法的期望成本进行了分析，如使用标签成本的活跃学习，适用于多类和部分标签查询的活跃学习，以及批处理模式的活跃学习。