Feature Selection for Multimedia Analysis by Sharing Information Among Multiple Tasks
Feature Selection for Multimedia Analysis by Sharing Information Among Multiple Tasks
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DOI：
10.1109/tmm.2012.2237023
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发表时间：
2013-04
影响因子：
7.3
通讯作者：
Yi Yang;Zhigang Ma;Alexander Hauptmann;N. Sebe
中科院分区：
文献类型：
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作者：
Yi Yang;Zhigang Ma;Alexander Hauptmann;N. Sebe
虽然在多任务分类和子空间学习方面已经取得了很大进展，但多任务特征选择在很大程度上长期未得到解决。在本文中，我们提出了一种新的多任务特征选择算法，并将其应用于多媒体（例如视频和图像）分析。我们的算法不是单独评估每个特征的重要性，而是以批量模式选择特征，从而考虑到特征相关性。虽然特征选择已经受到了很多研究关注，但通过利用来自多个相关任务的共享知识来提高特征选择性能方面所做的努力较少。我们的算法基于不同相关任务具有共同结构这一假设。在一个联合框架中同时学习不同任务的多个特征选择函数，这使得我们的算法能够利用多个任务的共有知识作为辅助信息来促进决策。提出了一种有效的迭代算法对其进行优化，并保证了其收敛性。在不同数据库上的实验证明了所提算法的有效性。
While much progress has been made to multi-task classification and subspace learning, multi-task feature selection has long been largely unaddressed. In this paper, we propose a new multi-task feature selection algorithm and apply it to multimedia (e.g., video and image) analysis. Instead of evaluating the importance of each feature individually, our algorithm selects features in a batch mode, by which the feature correlation is considered. While feature selection has received much research attention, less effort has been made on improving the performance of feature selection by leveraging the shared knowledge from multiple related tasks. Our algorithm builds upon the assumption that different related tasks have common structures. Multiple feature selection functions of different tasks are simultaneously learned in a joint framework, which enables our algorithm to utilize the common knowledge of multiple tasks as supplementary information to facilitate decision making. An efficient iterative algorithm is proposed to optimize it, whose convergence is guaranteed. Experiments on different databases have demonstrated the effectiveness of the proposed algorithm.