Efficient Fisher Discrimination Dictionary Learning

doi:10.1016/j.sigpro.2016.03.013

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Efficient Fisher Discrimination Dictionary Learning
	Jiang, Rui1 ; Qiao, Hong1,2 ; Zhang, Bo 3,4; Qiao H(乔红)
发表期刊	SIGNAL PROCESSING
	2016-11-01
卷号	128 期号:1 页码:28-39
文章类型	Article
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.
其他摘要	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。
关键词	Fisher Discrimination Dictionary Learning Nesterov's Accelerated Gradient Method Face Recognition Domain Adaptation
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.sigpro.2016.03.013
关键词[WOS]	CONSISTENT K-SVD ; SPARSE REPRESENTATION ; FACE RECOGNITION ; ALGORITHMS
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61210009 ; 61033011 ; 61379093 ; 11131006)
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000379706500004
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12152
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
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
第一作者单位	中国科学院自动化研究所
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.

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