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A Multi-Class Multi-Manifold learning Algorithm based on ISOMAP

A Multi-Class Multi-Manifold Learning Algorithm based on ISO...

引用

Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Cheng, Qicai Wang, Hongyuan Feng, Yan Liu, Suolan Xue, Lei Jiangsu Polytech Univ Sch Informat Changzhou 213164 Peoples R China

ISBN: (纸本)9781424441990

The classical algorithm ISOMAP can find the intrinsic low-dimensional structures hidden in high-dimensional data uniformly distributed on or around a single manifold, but if the data are sampled from multi-class, each of which corresponds to an independent manifold, and clusters formed by data points belonging to each class are separated away, several disconnected neighborhood graphs will form, which leads to the failure of ISOMAP algorithm. In this paper, an improved version of ISOMAP, namely Multi-Class Multi-Manifold ISOMAP (MCMM-ISOMAP), is proposed. MCMM-ISOMAP constructs a single neighborhood graph not by increasing the value of neighborhood parameter, but by the following steps that first choose appropriate value with which short-circuit edges can not be introduced, second find such pail-wise data each of which are two endpoints of the shortest Euclidean distance between classes, and finally make them neighborhood points each other. Thereby a single neighborhood graph will form, and then ISOMAP algorithm is applied to find the intrinsic low-dimensional embedding structure. Experimental results on synthetic and real data reveal effectiveness of the proposed method.

关键词： ISOMAP multi-class multi-manifold MCMM-ISOMAP manifold learning

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Proceedings of the 2009 Chinese Conference on pattern recognition, CCPR 2009, and the 1st CJK Joint workshop on pattern recognition, CJKPR

Proceedings of the 2009 Chinese Conference on Pattern Recogn...

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2009 Chinese Conference on pattern recognition, CCPR 2009 and the 1st CJK Joint workshop on pattern recognition, CJKPR

ISBN: (纸本)9781424441990

The proceedings contain 202 papers. The topics discussed include: local search algorithm for K-means clustering based on minimum sub-cluster size;a discretization algorithm of continuous attributes based on supervised clustering;a modified differential evolution algorithm for multi-objective optimization problems;collaborative filtering in personalized recommendation based on users pattern subspace clustering;the generic object classification based on MIML machine learning;cluster based multi-populations genetic algorithm in noisy environment;feature selection for classifying data stream based on maximum entropy;kernel-plural discriminant analysis based on Fourier transform and its application to face recognition;local graph embedding discriminant analysis for face recognition with single training sample per person;two-dimensional local graph embedding discriminant analysis(F2DLGEDA) with its application to face and palm biometrics;and gait recognition based on multi-resolution regional shape context.

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Marginal and Nonlocal Discriminant Embedding for Face recognition

Marginal and Nonlocal Discriminant Embedding for Face Recogn...

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Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Lai, Zhihui Jin, Zhong Davoine, Franck Nanjing Univ Sci & Technol Sch Comp Sci Nanjing 210094 Jiansu Peoples R China Inst Automat LIAMA Sino French IT Lab Beijing 100080 Peoples R China

ISBN: (纸本)9781424441990

This paper develops a supervised discriminant technique, called marginal and nonlocal discriminant embedding (MNDE), for dimensionality reduction of high-dimensional data in small sample size problems. MNDE can be seen as a linear approximation of a multimanifold-based learning framework in which nonlocal property is taken into account besides the marginal property and local property. MNDE seeks to find a set of perfect projections that not only can impact the samples of intraclass and maximize the margin of interclass, but also can simultaneously maximize the nonlocal scatter that characterizes the sum scatter of any pair of data out of local K-neighborhood. This characteristic makes MNDE more intuitive and more powerful than LDA and Marginal Fisher Analysis (MFA). The proposed method is applied to face recognition and is examined on the Yale and AR face image databases.

关键词： Graph Embedding Face recognition Manifold learning

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An anti-compression watermarking scheme for vector map based on improved Douglas-Peucker algorithm

An anti-compression watermarking scheme for vector map based...

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1st international workshop on Education Technology and Computer Science, ETCS 2009

作者： Zhang, Zuoli Wang, Yaming Sun, Shusen Research Institute of Computer Vision and Pattern Recognition ZheJiang Sci.-Tech. University Hangzhou China School of Information and Electronics ZheJiang Sci.-Tech. University Hangzhou China

ISBN: (纸本)9780769535579

An anti-compression watermarking scheme is proposed. The scheme uses improved Dougleas-Peucker algorithm to compress redundant vertex data, which is important character of vector map, and embeds the watermark into compressed vertex data. Experimental results show that the anticompression performance of the watermarking algorithm is satisfied. © 2009 IEEE.

关键词： pattern matching

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Face recognition Using Affine Subspace Nearest Points Approach

Face Recognition Using Affine Subspace Nearest Points Approa...

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Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Zhou, Xiaofei Jiang, Wenhan Shi, Yong Chinese Acad Sci Grad Univ Beijing 100190 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China First Res Inst Minist Publ Secur Beijing 100048 Peoples R China Univ Nebraska Omaha Coll Informat Sci & Technol Omaha NE 68182 USA

ISBN: (纸本)9781424441990

This paper proposed a novel classification approach, called affine subspace nearest points (ASNP) approach, for face recognition. Inspired by the nearest point problem of SVM which computes the nearest points between two convex hulls, ASNP replaces the convex hulls with the affine subspaces of each class samples, i.e., ASNP approach constructs the two smallest subspaces which respectively contain the data in each class, and finds the closest points in two subspaces. Then, the plane, which bisects the line segment connecting the two points, is constructed as the separating plane. Compared with SVM, ASNP is simply a linear optimal problem, and it avoids the convex quadratic programming problem. We apply the ASNP approach on face recognition. The experiments on ORL face database, Yale face database and Harvard face database show that the performances of ASNP algorithms are competitive to l-NN classifier and SVM classifier for face classification.

关键词： classification pattern recognition machine learning affine subspace nearest point SVM

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GK-means: An Efficient K-means Clustering Algorithm Based On Grid

GK-means: An Efficient K-means Clustering Algorithm Based On...

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1st international Symposium on Computer Network and Multimedia Technology

作者： Chen, Xiaoyun Su, Youli Chen, Yi Liu, Guohua Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

ISBN: (纸本)9781424452729

As an important tool, clustering analysis is used in many applications such as pattern recognition, data mining, machine learning and statistics etc. K-means clustering, based on minimizing a formal objective function, is perhaps the most widely used and studied. But k the number of clusters needs users specify and the effective initial centers are difficult to select. Meanwhile, it is sensitive to noise data points. In this paper, we focus on choice the better initial centers to improve the quality of k-means and to reduce the computational complexity of k-means method. The proposed algorithm called GK-means, which combines grid structure and spatial index with k-means algorithm. Theoretical analysis and experimental results show the algorithm has high quality and efficiency.

关键词： data mining clustering analysis k-means algorithm grid technology

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Video shot segmentation using graph-based dominant-set clustering 09

Video shot segmentation using graph-based dominant-set clust...

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1st international Conference on Internet Multimedia Computing and Service, ICIMCS 2009

作者： Li, Li Zeng, Xianglin Li, Xi Hu, Weiming Zhu, Pengfei National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781605588407

Video shot segmentation is a solid foundation for automatic video content analysis, for most content based video retrieval tasks require accurate segmentation of video boundaries. In recent years, using the tools of data mining and machine learning to detect shot boundaries has become more and more popular. In this paper, we propose an effective video segmentation approach based on a dominant-set clustering algorithm. The algorithm can not only automatically determine the number of video shots, but also obtain accurate shot boundaries with low computation complexity. Experimental results have demonstrated the effectiveness of the proposed shot segmentation approach. Copyright 2009 ACM.

关键词： Clustering algorithms

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Anomaly Detection System using Resource pattern learning

Anomaly Detection System using Resource Pattern Learning

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1st international workshop on Software Technologies for Future Dependable Distributed Systems

作者： Ohno, Yuki Sugaya, Midori van der Zee, Andrej Nakajima, Tatsuo Waseda Univ Dept Comp Sci & Engn Tokyo Japan Japan Sci & Technol Agcy Dependable Embedded OS Ctr Tokyo Japan

ISBN: (纸本)9781424435654

In this paper, Anomaly Detection by Resource Monitoring (Ayaka), a novel lightweight anomaly and fault detection infrastructure, is presented for Information Appliances. Ayaka provides a general monitoring method for detecting anomalies using only resource usage information on systems independent of its domain, target application and programming languages. Ayaka modifies the kernel to detect faults and uses a completely application black-box approach based on machine learning methods. It uses the clustering method to quantize the resource usage vector data and learn the normal patterns with Hidden Markov Model. In the running phase, Ayaka finds anomalies by comparing the application resource usage with learned model. The evaluation experiment indicates that our prototype system is able to detect anomalies, such as SQL injection and buffer overrun, without significant overheads.

关键词： Anomaly Detection Dependability Hidden Markov Model machine learning

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1st ACM international workshop on Privacy and Anonymity for Very Large datasets, PAVLAD'09, Co-located with the 18th ACM international Conference on Information and Knowledge Management, CIKM 2009

1st ACM International Workshop on Privacy and Anonymity for ...

引用

1st ACM international workshop on Privacy and Anonymity for Very Large datasets, PAVLAD'09, Co-located with the 18th ACM international Conference on Information and Knowledge Management, CIKM 2009

ISBN: (纸本)9781605588049

The proceedings contain 8 papers. The topics discussed include: a new framework to automate constrained microaggregation;record linkage performance for large data sets;SAX: a privacy preserving general pupose method applied to detection of intrusions;applying differential privacy to search queries in a policy based interactive framework;weighted network decapitation: the economics of iterated attack and defense;incremental privacy preservation for associative classification;a novel approach for privacy mining of generic basic association rules;and on privacy preservation in text and document-based active learning for named entity recognition.

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Proceedings - 2009 1st international workshop on database Technology and Applications, DBTA 2009

Proceedings - 2009 1st International Workshop on Database Te...

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2009 1st international workshop on database Technology and Applications, DBTA 2009

ISBN: (纸本)9780769536040

The proceedings contain 168 papers. The topics discussed include: research on automatic control system based on 6K controller welding robot;implementation of embedded web server based on Mc9s12ne64;effective communication on the prevention of psychological contract breach;knowledge construction based ion visualization e-learning in digital library;global spatial data mining basing on web geological database;an improving and application of attribute reduction arithmetic in agroclimatology;effect of navigation aids and landmarks on acquisition of spatial knowledge in virtual environments;research on performance analysis and comprehensive evaluation model of MES for machining workshop;expropriation of the biggest shareholdings based on principal component analysis in neural networks;empirical analysis of Beijing logistics industry's GDP contribution to the national economy;and road surface condition recognition method based on color models.

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