The dissimilarity space: Bridging structural and statistical pattern recognition

被引:58
作者
Duin, Robert P. W. [1 ]
Pekalska, Elzbieta [2 ]
机构
[1] Delft Univ Technol, Pattern Recognit Lab, Delft, Netherlands
[2] Univ Manchester, Sch Comp Sci, Manchester M13 9PL, Lancs, England
基金
英国工程与自然科学研究理事会;
关键词
Dissimilarity representation; Representation set; Dissimilarity space; Vector space; Structural pattern recognition; VECTOR-SPACES; CLASSIFICATION;
D O I
10.1016/j.patrec.2011.04.019
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Human experts constitute pattern classes of natural objects based on their observed appearance. Automatic systems for pattern recognition may be designed on a structural description derived from sensor observations. Alternatively, training sets of examples can be used in statistical learning procedures. They are most powerful for vectorial object representations. Unfortunately, structural descriptions do not match well with vectorial representations. Consequently it is difficult to combine the structural and statistical approaches to pattern recognition. Structural descriptions may be used to compare objects. This leads to a set of pairwise dissimilarities from which vectors can be derived for the purpose of statistical learning. The resulting dissimilarity representation bridges thereby the structural and statistical approaches. The dissimilarity space is one of the possible spaces resulting from this representation. It is very general and easy to implement. This paper gives a historical review and discusses the properties of the dissimilarity space approaches illustrated by a set of examples on real world datasets. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码:826 / 832
页数:7
相关论文
共 33 条
[1]  
Alimoglu F, 1997, PROC INT CONF DOC, P637, DOI 10.1109/ICDAR.1997.620583
[2]  
[Anonymous], 1991, CONSCIOUSNESS EXPLAI
[3]  
[Anonymous], 2005, The Dissimilarity Representation for Pattern Recognition
[4]  
Calana Yenisel Plasencia, 2010, Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR 2010), P177, DOI 10.1109/ICPR.2010.52
[5]  
CANU S, 2003, ADV LEARNING THEORY, V190, P89
[6]   LIBSVM: A Library for Support Vector Machines [J].
Chang, Chih-Chung ;
Lin, Chih-Jen .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
[7]  
Cristianini Nello, 2000, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, DOI DOI 10.1017/CB09780511801389
[8]  
Duin RPW, 2010, LECT NOTES COMPUT SC, V6388, P46, DOI 10.1007/978-3-642-17711-8_5
[9]  
Edelman Shimon., 1999, REPRESENTATION RECOG
[10]  
Fu K.S., 1982, SYNTACTIC PATTERN RE, Vsecond