Pattern Recognition With Weighted Complex Networks

doi:10.1103/PhysRevE.78.056107

IMECH-IR > 力学所知识产出(1956-2008)

	Pattern Recognition With Weighted Complex Networks
	Cheh J; Zhao H; Cheh, J (reprint author), Xiamen Univ, Dept Phys, Inst Theoret Phys & Astrophys, Xiamen 361005, Peoples R China.
发表期刊	Physical Review E
	2008
ISSN	1539-3755
摘要	In this paper we introduce a weighted complex networks model to investigate and recognize structures of patterns. The regular treating in pattern recognition models is to describe each pattern as a high-dimensional vector which however is insufficient to express the structural information. Thus, a number of methods are developed to extract the structural information, such as different feature extraction algorithms used in pre-processing steps, or the local receptive fields in convolutional networks. In our model, each pattern is attributed to a weighted complex network, whose topology represents the structure of that pattern. Based upon the training samples, we get several prototypal complex networks which could stand for the general structural characteristics of patterns in different categories. We use these prototypal networks to recognize the unknown patterns. It is an attempt to use complex networks in pattern recognition, and our result shows the potential for real-world pattern recognition. A spatial parameter is introduced to get the optimal recognition accuracy, and it remains constant insensitive to the amount of training samples. We have discussed the interesting properties of the prototypal networks. An approximate linear relation is found between the strength and color of vertexes, in which we could compare the structural difference between each category. We have visualized these prototypal networks to show that their topology indeed represents the common characteristics of patterns. We have also shown that the asymmetric strength distribution in these prototypal networks brings high robustness for recognition. Our study may cast a light on understanding the mechanism of the biologic neuronal systems in object recognition as well.
关键词	Neural-networks Associative Memory Performance Components Topology
DOI	10.1103/PhysRevE.78.056107
收录类别	SCI
语种	英语
WOS记录号	WOS:000261213800015
关键词[WOS]	NEURAL-NETWORKS ; ASSOCIATIVE MEMORY ; PERFORMANCE ; COMPONENTS ; TOPOLOGY
WOS研究方向	Physics
WOS类目	Physics, Fluids & Plasmas ; Physics, Mathematical
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://dspace.imech.ac.cn/handle/311007/25822
专题	力学所知识产出(1956-2008)
通讯作者	Cheh, J (reprint author), Xiamen Univ, Dept Phys, Inst Theoret Phys & Astrophys, Xiamen 361005, Peoples R China.
推荐引用方式 GB/T 7714	Cheh J,Zhao H,Cheh, J . Pattern Recognition With Weighted Complex Networks[J]. Physical Review E,2008.
APA	Cheh J,Zhao H,&Cheh, J .(2008).Pattern Recognition With Weighted Complex Networks.Physical Review E.
MLA	Cheh J,et al."Pattern Recognition With Weighted Complex Networks".Physical Review E (2008).

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