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On alpha-mean of Kullback-Leibler divergences for subspace selection

On alpha-mean of Kullback-Leibler divergences for subspace s...

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2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Chen, Si-Bao Wang, Hai-Xian Zhang, Xing-Yi Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China Key Lab of Child Development and Learning Science of Ministry of Education Research Center for Learning Science Southeast University Nanjing Jiangsu 210096 China

ISBN: (纸本)9781612841816

Fisher's linear discriminant analysis (FLDA) is one of the most well-known linear subspace selection methods. However, FLDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. Recent results show that maximizing the geometric mean or harmonic mean of Kullback-Leibler (KL) divergences of class pairs can significantly reduce this problem. In this paper, to further reduce the class separation problem, the alpha-mean of divergences as a framework is proposed for subspace selection, with arithmetic mean, geometric mean and harmonic mean as special cases. We named it the maximization of the alpha-mean of all pairs of KL divergences (MAMD) criterion. A quasi-Newton method is applied to solve the optimization problem. Experiments on synthetic data and two datasets in UCI machine learning repository show the validity of MAMD. © 2011 IEEE.

关键词： Newton-Raphson method

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A Classification Framework of Service Composition Issues in BIRIS Scenario

A Classification Framework of Service Composition Issues in ...

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International Joint Conference on Service sciences (IJCSS)

作者： Dianhui Chu Zhongjie Wang Xiaofei Xu Research Center of Intelligent Computing for Enterprises and Services (ICES) School of Computer Science and Technology Harbin Institute of Technology Harbin China

Service composition (SC) is currently one of the most popular research topics in service computing domain. Given a specific functional and non-functional requirements on a specific service, a composition algorithm selects a set of service components from an existing repository and composes them together as a total solution to fulfill the requirement. There have been fruitful achievements, e.g., template-based composition, planning-based composition, automated/semi-automated/manual composition, etc. However, along with BIRIS becomes a dominate form of modern service systems, a series of new appearing features bring great challenges to traditional SC researches, e.g., (1) from one single requirement to temporally sequential requirements, (2) the available services from abundant to limited, (3) from orchestration-oriented composition solution to choreography-oriented ones, (4) from specified requirements to ambiguous and personalized ones, (5) from the objective of maximizing customer satisfaction to ensuring the benefits of customers and providers together, etc. This paper analyzes these challenges elaborately and presents a classification framework which includes 4 dimensions and 13 sub-dimensions. Several typical SC issues in BIRIS scenario are identified under the framework.

关键词： Web services Optimization Quality of service Customer satisfaction Manuals

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The Quantitative Evaluation on the Value-Oriented Priority of Service Elements

The Quantitative Evaluation on the Value-Oriented Priority o...

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International Joint Conference on Service sciences (IJCSS)

作者： Chao Ma Xiaofei Xu Zhongjie Wang Research Center of Intelligent Computing for Enterprises and Services (ICES) School of Computer Science and Technology Harbin Institute of Technology Harbin China

Value is considered as the ultimate goal of a good service, and whether and to what degree the expected value could be delivered to customers and providers depends on capacities and quality of various functional service elements in service system. Different elements have different contributions to the value production. Due to the limit of available service components to construct the service system, it is necessary to measure the priority of each service element in service models so that those elements with higher value-oriented priorities (VOP) should be implemented by concrete service components earlier and with higher capability during the service system development. This paper presents a quantitative evaluation method of VOP. Basic definition and measurement of service value are firstly introduced, and then VOP is put forward following three types of particular priorities called sensitivity points, tradeoff points and key points. Their characteristics and evaluation principles are presented in details, and the process of the quantitative evaluation method is put forward subsequently. To complement the above discussion, a case study from ocean transportation service is applied for demonstration.

关键词： Quality of service Sea measurements Sensitivity Reliability Receivers Transportation Oceans

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Adaptive discriminant analysis for face recognition from single sample per person

Adaptive discriminant analysis for face recognition from sin...

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IEEE International Conference on Automatic Face and Gesture Recognition

作者： Kan, Meina Shan, Shiguang Su, Yu Chen, Xilin Gao, Wen Digital Media Research Center Institute of Computing Technology CAS Beijing China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Beijing China Department of Computer Science University of Caen France Institute for Digital Media Peking University Beijing China School of Computer Science and Technology Harbin Institute of Technology Beijing China

ISBN: (纸本)9781424491407

Discriminant analysis, especially Fisherface and its numerous variants, have achieved great success in face recognition. However, these methods fail to work for face recognition from Single Sample per Person (SSPP), since they need more than one sample per person to estimate the within-class scatter matrix. To break this inability of traditional discriminant analysis, our paper proposes Adaptive Discriminant Analysis (ADA). In our method, the within-class scatter matrix of each enrolled subject is estimated from his/her single sample, by inferring from a generic training set with multiple samples per person. The inference is inspired by a simple intuition that similar person follows similar within-class variations. Specifically, both kNN regression and Lasso regression are explored for this purpose. We evaluate our method on FERET database and a large real-world face database. The results are very impressive compared with dominant traditional solutions to SSPP problem. © 2011 IEEE.

关键词： Discriminant analysis

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面向同时多线程的可扩展高速缓存动态划分方法

面向同时多线程的可扩展高速缓存动态划分方法

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2012全国高性能计算学术年会

作者： WU Jun-Min 吴俊敏 ZHU Xiao-Dong 朱小东 SUI Xiu-Feng 隋秀峰 JIN Ying-Qi 金瑛棋 ZHAO Xiao-Yu 赵小雨 School of Computer Science and Technology University of Science and Technology of China Hefei 230 中国科学技术大学计算机学院合肥230027 中国科学技术大学苏州研究院院苏州215123 National Research Center for Intelligent Computing Systems Institute of Computing Technology Beiji 中国科学院计算技术研究所国家智能计算研究中心北京100091

提出一种面向大规模同时多线程处理器可扩展高速缓存的动态划分方法.将文献所提的机制扩展到多体高速缓存中.线程的内存访问特征变化迅速，因此在运行时收集同时多线程的缺失率特征，依此特征在线程间划分高速缓存.实验结果显示，相对... 详细信息

提出一种面向大规模同时多线程处理器可扩展高速缓存的动态划分方法.将文献所提的机制扩展到多体高速缓存中.线程的内存访问特征变化迅速，因此在运行时收集同时多线程的缺失率特征，依此特征在线程间划分高速缓存.实验结果显示，相对于非划分的具有标准LRU替换策略的高速缓存，本文提出的划分方法能够IPC达18.94％.

关键词：多线程处理器高速缓存动态划分

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Context-specific miRNA regulation network predicts cancer prognosis

Context-specific miRNA regulation network predicts cancer pr...

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IEEE International Conference on Systems Biology

作者： Xionghui Zhou Juan Liu Changning Liu Simon Rayner Fengji Liang Jingfang Ju Yinghui Li Shanguang Chen Jianghui Xiong School of Computer Science Wuhan University Wuhan China Bioinformatics Research Group Key Laboratory of Intelligent Information Processing Advanced Computing Research Laboratory Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China Bioinformatics Group State Key Laboratory of Virology Wuhan Institute of Virology Chinese Academy of Sciences (CAS) Wuhan China Bioinformatics Group and Data Coordination Center State Key Laboratory of Space Medicine Fundamentals and Application China Astronaut Research and Training Center Beijing China Department of Pathology Stony Brook University Medical Center New York USA

MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelled in the context of various diseases. Here we examine the dynamic alteration of context-specific miRNA regulation to determine whether modified microRNAs regulation on specific biological processes is a useful information source for predicting cancer prognosis. A new concept, Context-specific miRNA activity (CoMi activity) is introduced to describe the statistical difference between the expression level of a miRNA's target genes and non-targets genes within a given gene set (context).

关键词： Breast cancer Context Correlation Biological processes Metastasis Tumors

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A value-oriented model for managing service supply chains

A value-oriented model for managing service supply chains

引用

IEEE International Conference on Industrial Engineering and Engineering Management, IEEM2010

作者： He, T. Ho, W. Xu, X.F. Research Center of Intelligent Computing for Enterprises and Services School of Computer Science and Technology Harbin Institute of Technology Harbin China Operations and Information Management Group Aston Business School Aston University Birmingham United Kingdom

ISBN: (纸本)9781424485031

Service supply chain (SSC) has attracted more and more attention from academia and industry. Although there exists extensive product-based supply chain management models and methods, they are not applicable to the SSC as the differences between service and product. Besides, the existing supply chain management models and methods possess some common deficiencies. Because of the above reasons, this paper develops a novel value-oriented model for the management of SSC using the modeling methods of E3-value and Use Case Maps (UCMs). This model can not only resolve the problems of applicability and effectiveness of the existing supply chain management models and methods, but also answer the questions of 'why the management model is this?' and 'how to quantify the potential profitability of the supply chains?'. Meanwhile, the service business processes of SSC system can be established using its logic procedure. In addition, the model can also determine the value and benefits distribution of the entire service value chain and optimize the operations management performance of the service supply. ©2010 IEEE.

关键词： Supply chain management

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On dynamic weighting of data in clustering with K-alpha means

On dynamic weighting of data in clustering with K-alpha mean...

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2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Chen, Si-Bao Wang, Hai-Xian Luo, Bin Key Lab. of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui 230039 China Key Lab. of Child Development and Learning Science Research Center for Learning Science Southeast University Nanjing Jiangsu 210096 China

ISBN: (纸本)9780769541099

Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a new class of clustering algorithm, K-Alpha Means (KAM), which is insensitive to the initial centers. With K-Harmonic Means as a special case, KAM dynamically weights data points during iteratively updating centers, which deemphasizes data points that are close to centers while emphasizes data points that are not close to any centers. Through replacing minimum operator in K-Means by alpha-mean operator, KAM significantly improves the clustering performances. © 2010 IEEE.

关键词： Clustering algorithms

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Subspace selection using semi-supervised harmonic mean of Kullback-Leibler divergences

Subspace selection using semi-supervised harmonic mean of Ku...

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2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

作者： Chen, Si-Bao Wang, Hai-Xian Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui 230039 China Key Lab of Child Development and Learning Science of Ministry of Education Research Center for Learning Science Southeast University Nanjing Jiangsu 210096 China

ISBN: (纸本)9781424459346

In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), can significantly reduce the class separation problem. Furthermore, maximizing the harmonic mean of Kullback-Leibler (KL) divergences of class pairs (MHMD) emphasizes smaller divergences more than MGMD, and deals with the class separation problem more effectively. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate labeled data. To take advantage of unlabeled data for subspace selection, semi-supervised MHMD (SSMHMD) is proposed using graph Laplacian as normalization. Quasi-Newton method is adopted to solve the optimization problem. Experiments on synthetic data and real image data show the validity of SSMHMD. ©2010 IEEE.

关键词： Discriminant analysis

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Manifold alignment via corresponding projections

Manifold alignment via corresponding projections

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2010 21st British Machine Vision Conference, BMVC 2010

作者： Zhai, Deming Li, Bo Chang, Hong Shan, Shiguang Chen, Xilin Gao, Wen School of Computer Science and Technology Harbin Institute of Technology China Digital Media Research Center Institute of Computing Technology CAS China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences China Institute of Digital Media Peking University China

ISBN: (纸本)1901725405

In this paper, we propose a novel manifold alignment method by learning the underlying common manifold with supervision of corresponding data pairs from different observation sets. Different from the previous algorithms of semi-supervised manifold alignment, our method learns the explicit corresponding projections from each original observation space to the common embedding space everywhere. Benefiting from this property, our method could process new test data directly rather than re-alignment. Furthermore, our approach doesn't have any assumption on the data structures, thus it could handle more complex cases and get better results compared with previous work. In the proposed algorithm, manifold alignment is formulated as a minimization problem with proper constraints, which could be solved in an analytical manner with closed-form solution. Experimental results on pose manifold alignment of different objects and faces demonstrate the effectiveness of our proposed method. © 2010. The copyright of this document resides with its authors.

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