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Computing μ-bases from algebraic ruled surfaces

COMPUTER AIDED GEOMETRIC DESIGN 2016年第0期46卷 125-130页

作者： Shen, Li-Yong Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Acad Math & Syst Sci KLMM Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

We find a mu-basis for a rational ruled surface, starting from its implicit representation. A parametrization for this ruled surface is then deduced form this mu-basis. This parametrization does not have any non-generic base points and its directrix has the lowest possible degree. A complete interchange graph is built for the algebraic equation, the mu-basis and the parametric equation of a rational ruled surface. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Rational ruled surface Planar directrix mu-Basis Parametrization

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Evaluating Search Result Diversity using Intent Hierarchies 16

Evaluating Search Result Diversity using Intent Hierarchies

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39th International ACM SIGIR conference on Research and Development in Information Retrieval

作者： Wang, Xiaojie Dou, Zhicheng Sakai, Tetsuya Wen, Ji-Rong Renmin Univ China Sch Informat Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Waseda Univ Dept Comp Sci & Engn Tokyo Japan MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450340694

Search result diversification aims at returning diversified document lists to cover different user intents for ambiguous or broad queries. Existing diversity measures assume that user intents are independent or exclusive, and do not consider the relationships among the intents. In this paper, we introduce intent hierarchies to model the relationships among intents. Based on intent hierarchies, we propose several hierarchical measures that can consider the relationships among intents. We demonstrate the feasibility of hierarchical measures by using a new test collection based on TREC Web Track 2009-2013 diversity test collections. Our main experimental findings are: (1) Hierarchical measures are generally more discriminative and intuitive than existing measures using flat lists of intents;(2) When the queries have multilayer intent hierarchies, hierarchical measures are less correlated to existing measures, but can get more improvement in discriminative power;(3) Hierarchical measures are more intuitive in terms of diversity or relevance. The hierarchical measures using the whole intent hierarchies are more intuitive than only using the leaf nodes in terms of diversity and relevance.

关键词： Ambiguity Diversity Evaluation Novelty Hierarchy

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Genre-based Link prediction in Bipartite Graph for Music Recommendation

Genre-based Link prediction in Bipartite Graph for Music Rec...

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4th International Conference on Information Technology and Quantitative management (ITQM) - Promoting Business Analytics and Quantitative management of Technology

作者： Zhao, Daozhen Zhang, Lingling Zhao, Weiqi Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China

The bipartite graph method of link prediction can apply in many fields of recommendations, with the nodes (users and items) and links (interactions between users and items). However, that links cannot represent the users' dual preferences (like and dislike). Some researchers improved that limits by complex number representations, but still not consider the influence of users' similarity recommendation performance. Here, we proposed an improved method to cope with this deficiency, build the relational dualities by complex number representations and computing the users' similarity by genres weight relations. In experiments with the Xiami. com music dataset, the proposed music genre weight- based music recommendation model (MGW) performances better than the CORLP method. (C) 2016 The Authors. Published by Elsevier B. V.

关键词： Music recommendation Link prediction Music genres

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A Novel Framework for Online Amnesic Trajectory Compression in Resource-Constrained Environments

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IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2016年第11期28卷 2827-2841页

作者： Liu, Jiajun Zhao, Kun Sommer, Philipp Shang, Shuo Kusy, Brano Lee, Jae-Gil Jurdak, Raja Renmin Univ China Beijing 100872 Peoples R China CSIRO Data 61 Pullenvale Qld 4069 Australia Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China China Univ Petr Beijing 102249 Peoples R China Korea Adv Inst Sci & Technol Dept Knowledge Serv Engn Daejeon 34141 South Korea

State-of-the-art trajectory compression methods usually involve high space-time complexity or yield unsatisfactory compression rates, leading to rapid exhaustion of memory, computation, storage, and energy resources. Their ability is commonly limited when operating in a resource-constrained environment especially when the data volume (even when compressed) far exceeds the storage limit. Hence, we propose a novel online framework for error-bounded trajectory compression and ageing called the Amnesic Bounded Quadrant System (ABQS), whose core is the Bounded Quadrant System (BQS) algorithm family that includes a normal version (BQS), Fast version (FBQS), and a Progressive version (PBQS). ABQS intelligently manages a given storage and compresses the trajectories with different error tolerances subject to their ages. In the experiments, we conduct comprehensive evaluations for the BQS algorithm family and the ABQS framework. Using empirical GPS traces from flying foxes and cars, and synthetic data from simulation, we demonstrate the effectiveness of the standalone BQS algorithms in significantly reducing the time and space complexity of trajectory compression, while greatly improving the compression rates of the state-of-the-art algorithms (up to 45 percent). We also show that the operational time of the target resource-constrained hardware platform can be prolonged by up to 41 percent. We then verify that with ABQS, given data volumes that are far greater than storage space, ABQS is able to achieve 15 to 400 times smaller errors than the baselines. We also show that the algorithm is robust to extreme trajectory shapes.

关键词： Online trajectory compression ageing amnesic resource-constrained constant complexity

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The Study of Model for Power Grid Diagnosis based on Naive Bayes Theory

The Study of Model for Power Grid Diagnosis based on Naive B...

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IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Song, Wei Wu, Geng Zhu, Luyao Chi, Shaojie North China Elect Power Univ Sch Econ & Management Beijing Peoples R China UCAS CAS Res Ctr Fictitious Econ & Data Sci Key Lab Big Data Min & Knowledge Management CAS Sch Econ & Management Beijing Peoples R China Beijing Univ Technol Sch Econ & Management Beijing Peoples R China

ISBN: (纸本)9781509047710

State Grid Jibei Electric Power Company (Jibei) has accumulated a large number of operation data through the maintenance of the equipment, devices in power grids. It is of great significance to study this data and extract the useful information by the data mining and statistics techniques. While the theories and methods of Naive Bayes have been widely studied, this paper utilizes such technique to explore the model of power grid diagnosis, and analyzes the real data of recent years. This work is expected to drive the correct decision on the malfunctions of power grid and increase the efficiency of the operations.

关键词： Naive Bayes Power Grid Diagnosis Decision Analysis

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A divide-and-combine method for large scale nonparallel support vector machines

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NEURAL NETWORKS 2016年 75卷 12-21页

作者： Tian, Yingjie Ju, Xuchan Shi, Yong Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 101408 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

Nonparallel Support Vector Machine (NPSVM) which is more flexible and has better generalization than typical SVM is widely used for classification. Although some methods and toolboxes like SMO and libsvm for NPSVM are used, NPSVM is hard to scale up when facing millions of samples. In this paper, we propose a divide-and-combine method for large scale nonparallel support vector machine (DCNPSVM). In the division step, DCNPSVM divide samples into smaller sub-samples aiming at solving smaller subproblems independently. We theoretically and experimentally prove that the objective function value, solutions, and support vectors solved by DCNPSVM are close to the objective function value, solutions, and support vectors of the whole NPSVM problem. In the combination step, the sub-solutions combined as initial iteration points are used to solve the whole problem by global coordinate descent which converges quickly. In order to balance the accuracy and efficiency, we adopt a multi-level structure which outperforms state-of-the-art methods. Moreover, our DCNPSVM can tackle unbalance problems efficiently by tuning the parameters. Experimental results on lots of large data sets show the effectiveness of our method in memory usage, classification accuracy and time consuming. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Support vector machine Nonparallel support vector machine Large scale Clustering Divide Combine

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A Survey on Feature Selection

A Survey on Feature Selection

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4th International Conference on Information Technology and Quantitative management (ITQM) - Promoting Business Analytics and Quantitative management of Technology

作者： Miao, Jianyu Niu, Lingfeng Univ Chinese Acad Sci Sch Math Sci Beijing 100019 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

Feature selection, as a dimensionality reduction technique, aims to choosing a small subset of the relevant features from the original features by removing irrelevant, redundant or noisy features. Feature selection usually can lead to better learning performance, i.e., higher learning accuracy, lower computational cost, and better model interpretability. Recently, researchers from computer vision, text mining and so on have proposed a variety of feature selection algorithms and in terms of theory and experiment, show the effectiveness of their works. This paper is aimed at reviewing the state of the art on these techniques. Furthermore, a thorough experiment is conducted to check if the use of feature selection can improve the performance of learning, considering some of the approaches mentioned in the literature. The experimental results show that unsupervised feature selection algorithms benefits machine learning tasks improving the performance of clustering. (C) 2016 The Authors. Published by Elsevier B. V.

关键词： feature selection machine learning unsupervised clustering

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Scale, congestion, efficiency and effectiveness in e-commerce firms

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ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS 2016年 20卷 171-182页

作者： Yang, Zhuofan Shi, Yong Yan, Hong Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Res Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Hong Kong Polytech Univ Dept Logist & Maritime Studies Fac Business Hong Kong Hong Kong Peoples R China

This research examines what affects operations efficiency and explains the paradox of high efficiency and low profits using a two-stage analytic framework. It provides a brief overview of efficiency evaluation research for e-commerce, and establishes a set of efficiency evaluation criteria, with the application of data envelopment analysis (DEA). The results suggest that e-commerce firms suffer from input congestion. Also, scale and technology issues, as well as low economic effectiveness, lead to the overall operational inefficiency. Business-to-business (B2B) e-commerce was more efficient and effectiveness in 2014, while online-to-offline (O2O) commerce had steadier and faster growth. In general, the performance of e-commerce firms is becoming better over time. The findings of this research can help managers to improve their firms' efficiency levels by resizing their operational scale, strengthening their technology infrastructure and applications, and improving their discretionary allocation of resources. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Congestion data envelopment analysis (DEA) E-commerce Efficiency Effectiveness Math programming Production economics Returns to scale

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Forecasting China's Service Outsourcing Development with an EMD-VAR-SVR Ensemble Method

Forecasting China's Service Outsourcing Development with an ...

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4th International Conference on Information Technology and Quantitative management (ITQM) - Promoting Business Analytics and Quantitative management of Technology

作者： Yi, Siqi Guo, Kun Chen, Zhensong Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big DataMin & Knowledge Management Beijing 100190 Peoples R China

Service outsourcing is playing a more and more important role in the world economy. With the fast development of information technology and service industry, China is witnessing an accelerated growth momentum of service outsourcing. In the first half year of 2015, the total service-outsourcing executive contracts value of China is increased by 9.7% to US $40.8 billion. Looking ahead 10 years, China's service outsourcing market will receive unprecedented opportunities and challenges, predicting the contract amount of service outsourcing can promote the healthy development of the industry. In this paper, an EMD-VAR-SVR ensemble method is proposed for predicting the executive amount of servicing outsourcing. In order to verify and test the forecasting capability of the proposed EMD-VAR-SVR ensemble method, the single VAR model and single SVR model are used as the benchmark models. The empirical result shows that the proposed ensemble method's prediction has the best effect. (C) 2016 Published by Elsevier B.V.

关键词： Service outsourcing Empirical mode decomposition (EMD) Vector auto-regression (VAR) Support vector regression (SVR) Forecasting

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ν-Structural Nonparallel Support Vector Machine for Pattern Classification

ν-Structural Nonparallel Support Vector Machine for Pattern...

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IEEE/WIC/ACM International Conference on Web Intelligence (WI)

作者： Chen, Dandan Tian, Yingjie Univ Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Sch Math Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

ISBN: (纸本)9781509047710

The nu-nonparallel support vector machine (nu-SNPSVM) for classification has the advantage of using a parameter. on controlling the number of support vectors. However, it ignores the prior structural information in data. In this paper, we propose a novel nonparallel classifier, named nu-Structural Nonparallel Support Vector Machine (nu-SNPSVM), for binary classification. Each model of nu-SNPSVM considers not only the compactness in both classes by the structural information but also the separability between classes, thus it can fully exploit prior knowledge to directly improve the algorithm's generalization capacity and is certainly superior to NPSVMs and other twin SVMs theoretically. Experimental results on lots of data sets show the effectiveness of our method.

关键词： Support vector machines

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