Efficient Fisher Discrimination Dictionary Learning
文献类型：期刊论文
| 作者 | Jiang, Rui1 ; Qiao, Hong1,2; Zhang, Bo3,4; Qiao H(乔红)
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| 刊名 | SIGNAL PROCESSING
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| 出版日期 | 2016-11-01 |
| 卷号 | 128期号:1页码:28-39 |
| 关键词 | Fisher discrimination dictionary learning Nesterov's accelerated gradient method Face recognition Domain adaptation |
| 通讯作者 | 乔红 |
| 中文摘要 |
为了设计适用于不同类别变化不平衡的分类任务的快速Fisher判别性字典学习（Efficient Fisher Discrimination Dictionary Learning，E-FDDL）算法，我们提出解一个近似的Fisher判别性稀疏表示（Fisher Discrimination based Sparse Representation，FDSR）问题，它的目标函数是原始FDDL算法中FDSR问题目标函数的上界。该近似FDSR（Approximate FDSR，AFDSR）问题考虑了判别性重构和协同性重构两方面的作用，并且稍稍重视后者的作用，这使得E-FDDL在处理同类别变化不均匀的分类任务时更加鲁棒。进一步地，A-FDSR问题的结构使得快速的优化策略适用于该问题，这又带来了E-FDDL的快速性。我们在人脸识别实验的结果证实了E-FDDL快速稳定的表现。 |
| 英文摘要 | Fisher Determination Dictionary Learning (FDDL) has shown to be effective in image classification. However, the Original FDDL (O-FDDL) method is time-consuming. To address this issue, a fast Simplified FDDL (S-FDDL) method was proposed. But S-FDDL ignores the role of collaborative reconstruction, thus having an unstable performance in classification tasks with unbalanced changes in different classes. This paper focuses on developing an Efficient FDDL (E-FDDL) method, which is more suitable for such classification problems. Precisely, instead of solving the original Fisher Discrimination based Sparse Representation (FDSR) problem, we propose to solve an Approximate FDSR (A-FDSR) problem whose objective function is an upper bound of that of FDSR. A-FDSR considers the role of both the discriminative reconstruction and the collaborative reconstruction. This makes E-FDDL stable when dealing with classification tasks with unbalanced changes in different classes. Furthermore, fast optimization strategies are applicable to A-FDSR, thus leading to the high efficiency of E-FDDL which can be explained by analysis on convergence rate and computational complexity. We also use E-FDDL to accelerate the Shared Domain-adapted Dictionary Learning (SDDL) algorithm which is a FDDL based new method for domain adaptation. Experimental results on face and object recognition demonstrate the stable and fast performance of E-FDDL. (C) 2016 Elsevier B.V. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Engineering, Electrical & Electronic |
| 研究领域[WOS] | Engineering |
| 关键词[WOS] | CONSISTENT K-SVD ; SPARSE REPRESENTATION ; FACE RECOGNITION ; ALGORITHMS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000379706500004 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12152]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 通讯作者 | Qiao H(乔红) |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China 3.Chinese Acad Sci, LSEC, Beijing 100190, Peoples R China 4.Chinese Acad Sci, AMSS, Inst Appl Math, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Rui,Qiao, Hong,Zhang, Bo,et al. Efficient Fisher Discrimination Dictionary Learning[J]. SIGNAL PROCESSING,2016,128(1):28-39. |
| APA | Jiang, Rui,Qiao, Hong,Zhang, Bo,&乔红.(2016).Efficient Fisher Discrimination Dictionary Learning.SIGNAL PROCESSING,128(1),28-39. |
| MLA | Jiang, Rui,et al."Efficient Fisher Discrimination Dictionary Learning".SIGNAL PROCESSING 128.1(2016):28-39. |
入库方式： OAI收割
来源：自动化研究所
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