期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 21, 期 9, 页码 2292-2304出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2019.2900166
关键词
Dictionary learning; multiple kernel learning; discriminative projections; domain adaptation
资金
- Natural Science Foundation of China [61806099, 61672293]
- Natural Science Foundation of Jiangsu Province, China [BK20180790]
- Talent Start Foundation of Nanjing University of Information Science and Technology [2243141701077]
- PAPD (priority academic program development of Jiangsu Higher Education Institutions)
Dictionary learning has produced state-of-the-art results in various classification tasks. However, if the training data have a different distribution than the testing data, the learned sparse representation might not be optimal. Recently, several domain-adaptive dictionary learning (DADE.) methods and kernels have been proposed and have achieved impressive performance. However, the performance of these single kernel-based methods heavily depends heavily on the choice of the kernel, and the question of how to combine multiple kernel learning (MKL) with the DADL framework has not been well studied. Motivated by these concerns, in this paper, we propose a multi-kernel domain-adaptive sparse representation-based classification (MK-DASRC) and then use it as a criterion to design a multi-kernel sparse representation-based domain-adaptive discriminative projection method, in which the discriminative features of the data in the two domains are simultaneously learned with the dictionary. The purpose of this method is to maximize the between-class sparse reconstruction residuals of data from both domains, and minimize the within-class sparse reconstruction residuals of data in the low-dimensional subspace. Thus, the resulting representations can satisfactorily fit MK-DASRC and simultaneously display discriminability. Extensive experimental results on a series of benchmark databases show that our method performs better than the state-of-the-art methods.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据