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Representative Vector Machines: A Unified Framework for Classical Classifiers

文献类型：期刊论文

作者Gui, Jie1,2; Liu, Tongliang3,4; Tao, Dacheng3,4; Sun, Zhenan2; Tan, Tieniu2
刊名IEEE TRANSACTIONS ON CYBERNETICS
出版日期2016-08-01
卷号46期号:8页码:1877-1888
关键词Discriminant Vector Machine (Dvm) Pattern Classification Representative Vector Machines (Rvms) Sparse Representation Support Vector Machines (Svms)
DOI10.1109/TCYB.2015.2457234
文献子类Article
英文摘要Classifier design is a fundamental problem in pattern recognition. A variety of pattern classification methods such as the nearest neighbor (NN) classifier, support vector machine (SVM), and sparse representation-based classification (SRC) have been proposed in the literature. These typical and widely used classifiers were originally developed from different theory or application motivations and they are conventionally treated as independent and specific solutions for pattern classification. This paper proposes a novel pattern classification framework, namely, representative vector machines (or RVMs for short). The basic idea of RVMs is to assign the class label of a test example according to its nearest representative vector. The contributions of RVMs are twofold. On one hand, the proposed RVMs establish a unified framework of classical classifiers because NN, SVM, and SRC can be interpreted as the special cases of RVMs with different definitions of representative vectors. Thus, the underlying relationship among a number of classical classifiers is revealed for better understanding of pattern classification. On the other hand, novel and advanced classifiers are inspired in the framework of RVMs. For example, a robust pattern classification method called discriminant vector machine (DVM) is motivated from RVMs. Given a test example, DVM first finds its k-NNs and then performs classification based on the robust M-estimator and manifold regularization. Extensive experimental evaluations on a variety of visual recognition tasks such as face recognition (Yale and face recognition grand challenge databases), object categorization (Caltech-101 dataset), and action recognition (Action Similarity LAbeliNg) demonstrate the advantages of DVM over other classifiers.
WOS关键词EXTREME LEARNING-MACHINE ; FEATURE LINE METHOD ; FACE RECOGNITION ; IMAGE CLASSIFICATION ; PATTERN-CLASSIFICATION ; SPARSE REPRESENTATION
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000379984500015
资助机构National Basic Research Program of China(2012CB316300) ; National Basic Research Program of China(2012CB316300) ; National Basic Research Program of China(2012CB316300) ; National Basic Research Program of China(2012CB316300) ; National Science Foundation of China(61420106015 ; National Science Foundation of China(61420106015 ; National Science Foundation of China(61420106015 ; National Science Foundation of China(61420106015 ; Post-Doctoral Science Foundation of China(2012M520021 ; Post-Doctoral Science Foundation of China(2012M520021 ; Post-Doctoral Science Foundation of China(2012M520021 ; Post-Doctoral Science Foundation of China(2012M520021 ; Australian Research Council(FT-130101457 ; Australian Research Council(FT-130101457 ; Australian Research Council(FT-130101457 ; Australian Research Council(FT-130101457 ; 61135002 ; 61135002 ; 61135002 ; 61135002 ; 2013T60195) ; 2013T60195) ; 2013T60195) ; 2013T60195) ; LP-140100569) ; LP-140100569) ; LP-140100569) ; LP-140100569) ; 61272333 ; 61272333 ; 61272333 ; 61272333 ; 61572463 ; 61572463 ; 61572463 ; 61572463 ; 61273272) ; 61273272) ; 61273272) ; 61273272) ; National Basic Research Program of China(2012CB316300) ; National Basic Research Program of China(2012CB316300) ; National Basic Research Program of China(2012CB316300) ; National Basic Research Program of China(2012CB316300) ; National Science Foundation of China(61420106015 ; National Science Foundation of China(61420106015 ; National Science Foundation of China(61420106015 ; National Science Foundation of China(61420106015 ; Post-Doctoral Science Foundation of China(2012M520021 ; Post-Doctoral Science Foundation of China(2012M520021 ; Post-Doctoral Science Foundation of China(2012M520021 ; Post-Doctoral Science Foundation of China(2012M520021 ; Australian Research Council(FT-130101457 ; Australian Research Council(FT-130101457 ; Australian Research Council(FT-130101457 ; Australian Research Council(FT-130101457 ; 61135002 ; 61135002 ; 61135002 ; 61135002 ; 2013T60195) ; 2013T60195) ; 2013T60195) ; 2013T60195) ; LP-140100569) ; LP-140100569) ; LP-140100569) ; LP-140100569) ; 61272333 ; 61272333 ; 61272333 ; 61272333 ; 61572463 ; 61572463 ; 61572463 ; 61572463 ; 61273272) ; 61273272) ; 61273272) ; 61273272)
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/30849]  
专题合肥物质科学研究院_中科院合肥智能机械研究所
作者单位1.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China
3.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia
4.Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia
推荐引用方式
GB/T 7714		 Gui, Jie,Liu, Tongliang,Tao, Dacheng,et al. Representative Vector Machines: A Unified Framework for Classical Classifiers[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(8):1877-1888.
APA Gui, Jie,Liu, Tongliang,Tao, Dacheng,Sun, Zhenan,&Tan, Tieniu.(2016).Representative Vector Machines: A Unified Framework for Classical Classifiers.IEEE TRANSACTIONS ON CYBERNETICS,46(8),1877-1888.
MLA Gui, Jie,et al."Representative Vector Machines: A Unified Framework for Classical Classifiers".IEEE TRANSACTIONS ON CYBERNETICS 46.8(2016):1877-1888.

入库方式： OAI收割

来源：合肥物质科学研究院

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