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Formal analysis of concept-interconnection

Journal of Computational information Systems 2005年第4期1卷 863-869页

作者： Lei, Yuxia Sui, Yuefei Cao, Baoxiang Wang, Yan Yu, Jiguo College of Computer Science and Technology Qufu Normal University Rizhao 276826 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

As a suitable tool for analyzing concept interconnection formally, the theory of Formal Concept Analysis (FCA) is applied. FCA deals with formal mathematical tools and techniques to develop and analyze relationship between concepts and to develop concept structures, and concepts are important building blocks in the concept-interconnection. This paper mainly discusses how FCA can be used to support concept-interconnection analysis from an application point of view. In order to introduce our idea, two kinds of concept-interconnection and interconnection measure in detail are discussed. One is based on Concept-Backbone and the other is based on the attributes. It is seen that FCA can support the building of concept-interconnection as a learning technique, but the established concept-interconnection also can be analyzed by using techniques of FCA.

关键词： Semantics

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Automatic gait recognition based on wavelet reflective symmetry moments

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Journal of information and Computational science 2005年第2期2卷 421-428页

作者： Zhao, Guoying Cui, Li Xiang, Shiming Li, Hua Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate School Chinese Academy of Sciences Beijing 100039 China School of Mathematics Science Beijing Normal University Beijing 100875 China

Gait is an identifying biometric feature. In recent years, Video-based gait recognition is becoming a new challenging problem in the field of computer vision. In this paper, wavelet reflective symmetry moments (WRSMs) have been proposed to describe and recognize gait automatically. WRSMs represent the appearance of people with merits of moments and wavelet analysis and reflect people's symmetrical walking habit. Moments have translation, scale and rotation invariant characteristics;while wavelet analysis is able to extract the multi-resolution features subtly and deal with noise. So combination of wavelet moments and reflective symmetry not only has characteristics of moments and wavelet analysis, but also is in accordance with one of the relative results in psychological researches which state that gait is a type of symmetrical model. Experiments based on USF's database have shown that the application of wavelet reflective symmetry moments in gait recognition leads to relatively high distinguishability of gait with effective noise handling.

关键词： Computer vision

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Fisher score based naïve Bayesian classifier

Fisher score based naïve Bayesian classifier

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2005 International Conference on Neural Networks and Brain Proceedings, ICNNB'05

作者： Zhongzhi, Shi Youping, Huang Sulan, Zhang Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate School Chinese Academy of Science Beijing 100039 China

ISBN: (纸本)0780394224

The naïve Bayesian classifier (NBC) is a simple yet very efficient classification technique in machine learning. But the unpractical condition independence assumption of NBC greatly degrades its performance. There are two primary ways to improve NBC's performance. One is to relax the condition independence assumption in NBC. This method improves NBC's accuracy by searching additional condition dependencies among attributes of the samples in a scope. It usually involves in very complex search algorithms. Another is to change the representation of the samples by creating new attributes from the original attributes, and construct NBC from these new attributes while keeping the condition independence assumption. Key problem of this method is to guarantee strong condition independencies among the new attributes. In the paper, a new means of making attribute set, which maps the original attributes to new attributes according to the information geometry and Fisher score, is presented, and then the FS-NBC on the new attributes is constructed. The condition dependence relation among the new attributes theoretically is discussed. We prove that these new attributes are condition independent of each other under certain conditions. The experimental results show that our method improves performance of NBC excellently. © 2005 IEEE.

关键词： Classification (of information)

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A novel feature extraction algorithm

A novel feature extraction algorithm

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Ding, Shi-Fei Shi, Zhong-Zhi Wang, Yun-Cheng Li, Shu-Shan College of Information Science and Engineering Shandong Agricultural University Taian 271018 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China College of Information Science and Engineering Shandong University of Science and Technology Qingdao 266510 China

ISBN: (纸本)078039092X

Feature extraction or selection is one of the most importmant steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propose a new concept of probability information distance (PID) and prove that the PID satisfies four requests of axiomatization of the distance. So the PID is a kind of distance measure, which can be used to measure the degree of variation between two random variables. We make the PID be separability criterion of the classes for information feature extraction, and call it PID criterion (PIDC). Based on PIDC, we design a novel algorithm for information feature xtraction. Compared with principal components analysis (PCA), correlation analysis etc., the algorithm put forward in this paper had regarded for the class information, and so it is a kind of supervised algorithm of feature extraction. The experimental results demonstrate that the algorithm is valid and reliable, and it provides a new research approach for feature extraction, data mining and pattern recognition. © 2005 IEEE.

关键词： Feature extraction

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information feature analysis and improved algorithm of PCA

Information feature analysis and improved algorithm of PCA

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Ding, Shi-Fei Shi, Zhong-Zhi Liang, Yong Jin, Feng-Xiang College of Information Science and Engineering Shandong Agricultural University Taian 271018 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China College of Geo-Information Science and Engineering Shandong University of Science and Technology Qingdao 266510 China

ISBN: (纸本)078039092X

Principal component analysis (PCA) is an important method in multivariate statistical analysis, and its main idea is compression of dimensionality including variables and samples. In this paper, based on the ideas concerned with information function and information entropy of Shannon information theory, consider the inherent characteristic of eigenvalues of matrix, two new concepts of possibility information function (PIF) and possibility information entropy (PIE) are proposed firstly. On the basis of these, the formulae of information rate (IR) and accumulated information rate (AIR) are set up, by which the degree of information compression is measured. In the end, we improve the PCA algorithm called improved principal component analysis (IPCA). Through simulated application in practice, the results show that the IPCA proposed here is efficient and satisfactory. It provides a new research approach of information feature compression for pattern recognition. © 2005 IEEE.

关键词： Principal component analysis

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Online infomax algorithm and its application to remove EEG artifacts

Online infomax algorithm and its application to remove EEG a...

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2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005

作者： Xiaopei, Wu Zhongfu, Ye Schoo1 of Information Science and Technology University of Science and Technology of China Hefei 230026 Anhui province China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 Anhui province China

ISBN: (纸本)0780387406

A online infomax algorithm is proposed in this paper. The performances and properties of this online algorithm is investigated in detail. To the problem of the artifacts removal in real life EEG signal, both the online-and batch infomax algorithm are applied and compared. The experiment results show that the online infomax algorithm proposed in this paper has the good performance both in artifacts removal and convergence in time-varying mixing system. © 2005 IEEE.

关键词： Algorithms

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Some marginal learning algorithms for unsupervised problems

Some marginal learning algorithms for unsupervised problems

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IEEE International Conference on Intelligence and Security Informatics, ISI 2005

作者： Tao, Qing Wu, Gao-Wei Wang, Fei-Yue Wang, Jue Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100080 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

In this paper, we investigate one-class and clustering problems by using statistical learning theory. To establish a universal framework, a unsupervised learning problem with predefined threshold η is formally described and the intuitive margin is introduced. Then, one-class and clustering problems are formulated as two specific η-unsupervised problems. By defining a specific hypothesis space in η-one-class problems, the crucial minimal sphere algorithm for regular one-class problems is proved to be a maximum margin algorithm. Furthermore, some new one-class and clustering marginal algorithms can be achieved in terms of different hypothesis spaces. Since the nature in SVMs is employed successfully, the proposed algorithms have robustness, flexibility and high performance. Since the parameters in SVMs are interpretable, our unsupervised learning framework is clear and natural. To verify the reasonability of our formulation, some synthetic and real experiments are conducted. They demonstrate that the proposed framework is not only of theoretical interest, but they also has a legitimate place in the family of practical unsupervised learning techniques. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Learning algorithms

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The architecture and implementation of resource space model system

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4th International Conference on Grid and Cooperative computing - GCC 2005

作者： Shi, Peng Xing, Yunpeng Yao, Erlin Wang, Zhen Yuan, Kehua Zhang, Junsheng Wang, Jianzeng Guo, Fei Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate School Chinese Academy of Sciences Beijing China Hunan Knowledge Grid Lab Hunan University of Science and Technology Hunan China

ISBN: (纸本)3540305106

Resource Space Model (RSM) is a semantic model to manage and share heterogeneous resources on the Internet. This paper focuses on the general architecture, physical implementation and application of RSM. The RSM system adopts Client/Server mode for users to utilize resource spaces through the Internet. The resource spaces are designed based on domain knowledge and stored as XML files in the server. The tool 3DBrowser provides a friendly interface with three-dimensional scene to display resource spaces, locate and operate on resources. Based on the architecture and tools, a Dunhuang RSM system has been developed as a collaboration platform to organize and share the resources of Dunhuang Culture Grid. The system provides an efficient resource locating way based on the classification of resources and improves the efficiency of research. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Semantics

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Using fuzzy cognitive map to effectively classify e-documents and application

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4th International Conference on Grid and Cooperative computing - GCC 2005

作者： Wang, Jianzeng Xing, Yunpeng Shi, Peng Guo, Fei Wang, Zhen Yao, Erlin Yuan, Kehua Zhang, Junsheng Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Hunan Knowledge Grid Lab Hunan University of Science and Technology Hunan China Graduate School Chinese Academy of Sciences Beijing China

ISBN: (纸本)3540305106

In the current Web, e-document has been the most common vehicle for delivering and exchanging information. As the amount of e-documents has grown enormously, effective classification facilities are urgently needed to classify and query e-documents users want. In this paper, we propose a method to classify e-documents into a set of predefined categories based on Fuzzy Cognitive Map (FCM). The e-documents are collected from Internet by a metasearch engine. FCM has been employed to capture the semantic relationships between keywords of e-documents. Experiments with a set of local e-documents have proved that this approach has high performance and can help users getting the e-documents efficiently and effectively. The proposed method has been implemented and integrated into the Dunhuang Feitian System to manage and classify e-documents. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Electronic document exchange

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Pushing scientific documents by discovering interest in information flow within E-science knowledge grid

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4th International Conference on Grid and Cooperative computing - GCC 2005

作者： Ding, Lianhong Li, Xiang Xing, Yunpeng Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate School of Chinese Academy of Sciences 100080 Beijing China Hunan Knowledge Grid Lab. Hunan University of Science and Technology Hunan China

ISBN: (纸本)3540305106

The Knowledge Grid is an intelligent and sustainable Internet application environment that enables people and roles to effectively capture, publish, share and manage explicit knowledge resources. As an important function of the e-science Knowledge Grid, this paper proposes an approach to effectively push scientific documents within research teams by detecting the social characteristics in self-organized network, discovering interest in information flow, and capturing dynamic changes of interests over time. The proposed approach can be used in any cooperative organizations. © Springer-Verlag Berlin Heidelberg 2005.

关键词： information analysis

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