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Fusion Tensor Subspace Transformation Framework
Wang, Su-Jing1,2,3; Zhou, Chun-Guang2; Fu, Xiaolan1; Wang, SJ (reprint author), Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing 100101, Peoples R China.
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摘要Tensor subspace transformation, a commonly used subspace transformation technique, has gained more and more popularity over the past few years because many objects in the real world can be naturally represented as multidimensional arrays, i.e. tensors. For example, a RGB facial image can be represented as a three-dimensional array (or 3rd-order tensor). The first two dimensionalities (or modes) represent the facial spatial information and the third dimensionality (or mode) represents the color space information. Each mode of the tensor may express a different semantic meaning. Thus different transformation strategies should be applied to different modes of the tensor according to their semantic meanings to obtain the best performance. To the best of our knowledge, there are no existing tensor subspace transformation algorithm which implements different transformation strategies on different modes of a tensor accordingly. In this paper, we propose a fusion tensor subspace transformation framework, a novel idea where different transformation strategies are implemented on separate modes of a tensor. Under the framework, we propose the Fusion Tensor Color Space (FTCS) model for face recognition.
学科领域Cognitive Neuroscience
2013
语种英语
发表期刊PLOS ONE
ISSN1932-6203
卷号8期号:7
期刊论文类型期刊论文
URL查看原文
收录类别SCI
项目简介This work was supported in part by grants from 973 Program (2011CB302201), the National Natural Science Foundation of China (61075042, 61175023), China Postdoctoral Science Foundation funded project (2012M520428) and the open project program (93K172013K04) of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
WOS关键词LOCALITY PRESERVING PROJECTIONS ; FACE RECOGNITION ; DISCRIMINANT-ANALYSIS ; COMPONENT ANALYSIS ; EIGENFACES ; PCA
WOS标题词Science & Technology
WOS研究方向Science & Technology - Other Topics
WOS类目Multidisciplinary Sciences
WOS记录号WOS:000321271900003
资助机构973 Program [2011CB302201] ; National Natural Science Foundation of China [61075042, 61175023] ; China Postdoctoral Science Foundation [2012M520428] ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University [93K172013K04]
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.psych.ac.cn/handle/311026/10817
专题脑与认知科学国家重点实验室
通讯作者Wang, SJ (reprint author), Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing 100101, Peoples R China.
作者单位1.Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing 100101, Peoples R China
2.Jilin Univ, Coll Comp Sci & Technol, Changchun 130023, Jilin, Peoples R China
3.Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130023, Jilin, Peoples R China
第一作者单位脑与认知科学国家重点实验室
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Wang, Su-Jing,Zhou, Chun-Guang,Fu, Xiaolan,et al. Fusion Tensor Subspace Transformation Framework[J]. PLOS ONE,2013,8(7).
APA Wang, Su-Jing,Zhou, Chun-Guang,Fu, Xiaolan,&Wang, SJ .(2013).Fusion Tensor Subspace Transformation Framework.PLOS ONE,8(7).
MLA Wang, Su-Jing,et al."Fusion Tensor Subspace Transformation Framework".PLOS ONE 8.7(2013).
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