期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 15, 期 3, 页码 661-669出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2012.2237023
关键词
Action recognition; image classification; multitask feature selection; 3D motion data annotation
资金
- National Science Foundation [IIS-0917072]
- National Institutes of Health (NIH) [1RC1MH090021-01]
- European Commission [FP7-248984 GLOCAL]
- National Program on Key Basic Research Project of China (973 Program) [2010CB327903]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0917072] Funding Source: National Science Foundation
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.
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