SubMIL: Discriminative subspaces for multi-instance learning

doi:10.1016/j.neucom.2015.08.089

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	SubMIL: Discriminative subspaces for multi-instance learning
	Yuan, Jiazheng 1,2; Huang, Xiankai 3; Liu, Hongzhe 1; Li, Bing4 ; Xiong, Weihua 4
发表期刊	NEUROCOMPUTING
	2016-01-15
卷号	173 页码:1768-1774
文章类型	Article
摘要	As an important learning scheme for Multi-Instance Learning (MIL), the Instance Prototype (IP) selection-based MIL algorithms transform bags into a new instance feature space and achieve impressed classification performance. However, the number of IPs in the existing algorithms linearly increases with the scale of the training data. The performance and efficiencies of these algorithms are easily limited by the high dimension and noise when facing a large scale of training data. This paper proposes a discriminative subspaces-based instance prototype selection method that is suitable for reducing the computation complexity for large scale training data. In the proposed algorithm, we introduce the low-rank matrix recovery technique to find two discriminative and clean subspaces with less noise; then present a l(2,1) norm-based self-expressive sparse coding model to select the most representative instances in each subspace. Experimental results on several data sets show that our algorithm achieves superior and stable performance but with lower dimension compared with other IP selection strategies. (C) 2015 Elsevier B.V. All rights reserved.
关键词	Multi-instance Learning Low Rank Subspace
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.neucom.2015.08.089
关键词[WOS]	ALGORITHM ; SELECTION
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61271369 ; Beijing Natural Science Foundation(4152016 ; National Key Technology RD Program(2014BAK08B02 ; Funding Project for Academic Human Resources Development in Beijing Union University(BPHR2014A04 ; Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges under Beijing Municipality(CITTCD 20130513 ; 61372148 ; 4152018) ; 2015BAH55F03) ; BPHR2014E02) ; IDHT 20140508) ; 61370038)
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000366879800129
引用统计	被引频次：4[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/173211/10645
专题	多模态人工智能系统全国重点实验室_模式分析与学习
作者单位	1.Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China 2.Beijing Union Univ, Comp Technol Inst, Beijing 100101, Peoples R China 3.Beijing Union Univ, Tourism Inst, Beijing 100101, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yuan, Jiazheng,Huang, Xiankai,Liu, Hongzhe,et al. SubMIL: Discriminative subspaces for multi-instance learning[J]. NEUROCOMPUTING,2016,173:1768-1774.
APA	Yuan, Jiazheng,Huang, Xiankai,Liu, Hongzhe,Li, Bing,&Xiong, Weihua.(2016).SubMIL: Discriminative subspaces for multi-instance learning.NEUROCOMPUTING,173,1768-1774.
MLA	Yuan, Jiazheng,et al."SubMIL: Discriminative subspaces for multi-instance learning".NEUROCOMPUTING 173(2016):1768-1774.

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