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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Li, Ruiying Zhang, Chunjie Huang, Qingming School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783319488899

The context of objects can provide auxiliary discrimination beyond objects. However, this effective information has not been fully explored. In this paper, we propose Tri-level Combination for Image Representation (TriCoIR) to solve the problem at three different levels: object intrinsic, strongly-related context and weakly-related context. Object intrinsic excludes external disturbances and more focuses on the objects themselves. Strongly-related context is cropped from the input image with a more loose bound to contain surrounding context. Weaklyrelated one is recovered from the image other than object for global context. First, strongly and weakly-related context are constructed from input images. Second, we make cascade transformations for more intrinsical object information, which depends on the consistency between generated global context and input images in the regions other than object. Finally, a joint representation is acquired based on these three level features. The experiments on two benchmark datasets prove the effectiveness of TriCoIR. © Springer International Publishing AG 2016.

关键词： Image representation

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Parameters Optimization for Nonparallel Support Vector Machine by Particle Swarm Optimization

Parameters Optimization for Nonparallel Support Vector Machi...

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

作者： Bamakan, Seyed Mojtaba Hosseini Wang, Huadong Ravasan, Ahad Zare Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ 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 Allameh Tabatabai Univ Dept Ind Management Tehran Iran

Support vector machine is a well-known and computationally powerful machine learning technique for pattern classification and regression problems, which has been successfully applied to solve many practical problems in a wide variety of fields. Nonparallel Support Vector Machine (NPSVM) which is an extension of Twin-SVMs, is proved to be theoretically and practically more flexible and superior than TWSVMs and also it overcomes several drawbacks of the existing typical SVMs in order to be applicable in large-scale data sets. However, one of the difficulties in successful implementation of NPSVM is its different parameters, which should be well adjusted during the training process. In fact, the generalization power, robustness and sparsity of NPSVM are extremely depended on well setting of its parameters. In this paper, we propose a hybrid approach for parameter determination of the NPSVM by Particle Swarm Optimization techniques. Furthermore, in order to increase the sparsity of NPSVM and to reduce the training time, we take into account the number of support vectors (SVs) along with classification accuracy as a weighted objective function. Our experiments on several public datasets show that the proposed method can achieve better classification accuracy compare to that of TWSVM and NPSVM with less computational time. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

关键词： Nonparallel Support Vector Machine Particle Swarm Optimization Twin SVM Parameter setting

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Laplacian SVM for Learning from label Proportions

Laplacian SVM for Learning from Label Proportions

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IEEE International Conference on data mining Workshops (ICDM Workshops)

作者： Limeng Cui Zhensong Chen Fan Meng Yong Shi School of Computer and Control Engineering UCAS School of Economics and Management UCAS Key Laboratory on Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9781509059119

Proportion-SVM has been deeply studied due to its broad application prospects, such as modeling voting behaviors and spam filtering. However, the geometric information has been widely ignored. Thus, current methods usually show sensitivity to noises. To address these problems, in this paper, we combine the proportion learning framework with Laplacian term. We exploit the advantages of Laplacian term. Extensive experiments show the effectiveness of our method.

关键词： Support vector machines Laplace equations Kernel Training Optimization data models data mining

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Exploring Outliers in Crowdsourced Ranking for QoE 17

Exploring Outliers in Crowdsourced Ranking for QoE

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25th ACM International Conference on Multimedia (MM)

作者： Xu, Qiandian Yan, Ming Huang, Chendi Xiong, Jiechao Huang, Qingming Yao, Yuan Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur SKLOIS Beijing 100093 Peoples R China Michigan State Univ Dept Computat Math Sci & Engn E Lansing MI 48824 USA Michigan State Univ Dept Math E Lansing MI 48824 USA Peking Univ Sch Math Sci BICMR LMAM LMEQF LMP Beijing 100871 Peoples R China Tencent AI Lab Shenzhen 518057 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Comput Tech Key Lab Intell Info Proc Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Hong Kong Univ Sci & Technol Dept Math Hong Kong 100871 Peoples R China

ISBN: (纸本)9781450349062

Outlier detection is a crucial part of robust evaluation for crowdsourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years. In this paper, we propose some simple and fast algorithms for outlier detection and robust QoE evaluation based on the nonconvex optimization principle. Several iterative procedures are designed with or without knowing the number of outliers in samples. Theoretical analysis is given to show that such procedures can reach statistically good estimates under mild conditions. Finally, experimental results with simulated and real-world crowdsourcing datasets show that the proposed algorithms could produce similar performance to Huber-LASSO approach in robust ranking, yet with nearly 8 or 90 times speed-up, without or with a prior knowledge on the sparsity size of outliers, respectively. Therefore the proposed methodology provides us a set of helpful tools for robust QoE evaluation with crowdsourcing data.

关键词： HodgeRank Outlier Detection l(0)-regularization Iterative Hard Thresholding Iterative Least Trimmed Squares Adaptive Algorithms

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Semi-supervised support vector classification with self-constructed Universum

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NEUROCOMPUTING 2016年 189卷 33-42页

作者： Tian, Yingjie Zhang, Ying Liu, Dalian 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 Univ Chinese Acad Sci Sch Math Sci Beijing 100190 Peoples R China Beijing Union Univ Dept Basic Course Teaching Beijing 100101 Peoples R China

In this paper, we propose a strategy dealing with the semi-supervised classification problem, in which the support vector machine with self-constructed Universum is iteratively solved. Universum data, which do not belong to either class of interest, have been illustrated to encode some prior knowledge by representing meaningful concepts in the same domain as the problem at hand. Our new method is applied to seek more reliable positive and negative examples from the unlabeled dataset step by step, and the Universum support vector machine(U-SVM) is used iteratively. Different Universum data will result in different performance, so several effective approaches are explored to construct Universum datasets. Experimental results demonstrate that appropriately constructed Universum will improve the accuracy and reduce the number of iterations. (C) 2016 Published by Elsevier B.V.

关键词： Semi-supervised Classification Universum Support vector machine

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Design and evaluation of multi-GPU enabled Multiple Symbol Detection algorithm

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JOURNAL OF SUPERCOMPUTING 2016年第6期72卷 2111-2131页

作者： Liu, Ying Zheng, Haixin Zhao, Renliang Jian, Liheng Univ Chinese Acad Sci Sch Comp & Control Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Acad Equipment Beijing Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China

Multiple Symbol Detection (MSD) is an important technique in digital signal processing. It estimates the sequence of the received signal by maximum-likelihood principle. Due to its high computational complexity, currently, MSD algorithms were implemented in specialized signal processing devices, such as Field Programmable Gate Arrays (FPGAs). As the rapid development of CUDA, GPU has successfully accelerated applications in a variety of domains. In this paper, we explore to utilize CUDA-enabled GPUs to accelerate MSD algorithm. The computation core of MSD, sliding correlation problem, is formulated and an efficient CUDA parallelization scheme is proposed. CUDA-enabled MSD (CU-MSD) algorithm is implemented by adapting CUDA-enabled sliding correlation. To further improve the scalability of CU-MSD, the implementation on multiple GPUs is proposed as well. Various optimization techniques are used to maximize the performance. The performance of CU-MSD is evaluated by an MSD-based demodulation for PCM/FM telemetry system. Four data sets from a real aerospace PCM/FM integrated baseband system were used in our experiments. The experimental results demonstrate up to 133.3 speedup using a single GPU and 514.64 speedup using 4 GPUs in a single server.

关键词： Parallel computing CUDA Multiple Symbol Detection Multi-GPU Demodulation Telemetry

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Laplacian SVM for Learning from label Proportions 16

Laplacian SVM for Learning from Label Proportions

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16th IEEE International Conference on data mining (ICDM)

作者： Cui, Limeng Chen, Zhensong Meng, Fan Shi, Yong UCAS Sch Comp & Control Engn Beijing Peoples R China UCAS Sch Econ & Management Beijing Peoples R China UCAS Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

ISBN: (纸本)9781509059102

关键词： Learning with label proportion Proportion-SVM Laplacian

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Beyond appearance model: Learning appearance variations for object tracking

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NEUROCOMPUTING 2016年 214卷 796-804页

作者： Li, Guorong Ma, Bingpeng Huang, Jun Huang, Qingming Zhang, Weigang Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100190 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100080 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

In this paper, we present a novel appearance variation prediction model which can be embedded into the existing generative appearance model based tracking framework. Different from the existing works, which online learn appearance model with obtained tracking results, we propose to predict appearance reconstruction error. We notice that although the learned appearance model can precisely describe the target in the previous frames, the tracking result is still not accurate if in the following frame, the patch that is most similar to appearance model is assumed to be the target. We first investigate the above phenomenon by conducting experiments on two public sequences and discover that in most cases the best target is not the one with minimal reconstruction error. Then we design three kinds of features which can encode motion, appearance, appearance reconstruction error information of target's surrounding image patches, and capture potential factors that may cause variations of target's appearance as well as its reconstruction error. Finally, with these features, we learn an effective random forest for predicting reconstruction error of the target during tracking. Experiments on various datasets demonstrate that the proposed method can be combined with many existing trackers and improve their performances significantly. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Object tracking Appearance model Appearance prediction

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Online Cooperative Promotion and Cost Sharing Policy under Supply Chain Competition

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MATHEMATICAL PROBLEMS IN ENGINEERING 2016年第1期2016卷 1-11页

作者： Erjiang, E. Peng, Geng Tian, Xin Chen, Qinghong Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Managements Beijing 100190 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Shanghai HEADING Informat Engn Co Ltd Shanghai 201112 Peoples R China

This paper studies online cooperative promotion and cost sharing decisions in competing supply chains. We consider amodel of one B2C e-commerce platform and two supply chains each consisting of a supplier and an online retailer. The problem is studied using a multistage game. Firstly, the e-commerce platform carries out the cooperative promotion and sets the magnitude of markdown (the value of e-coupon). Secondly, each retailer and his supplier determine the fraction of promotional cost sharing when they have different bargaining power. Lastly, the retailers decide whether to participate in the cooperative promotion campaign. We show that the retailers are likely to participate in the promotion if consumers become more price-sensitive. However, it does not imply that the retailers can benefit from the price promotion;the promotion decision game resembles the classical prisoner's dilemma game. The retailers and suppliers can benefit from the cooperative promotion by designing an appropriate cost sharing contract. For a supply chain, the bargaining power between supplier and retailer, consumer price sensitivity, and competition intensity affect the fraction of the promotional cost sharing. We also find that equilibrium value of e-coupon set by the e-commerce platform is not optimal for all the parties.

关键词： COST shifting SUPPLY chain management COMPETITION (Economics) ECONOMIC policy ELECTRONIC commerce

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How hyperbolic discounting preference affects Chinese consumers' consumption choice between conventional and electric vehicles

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ENERGY POLICY 2016年第0期97卷 400-413页

作者： Wu, Tian Shang, Zhe Tian, Xin Wang, Shouyang Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China 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 Peking Univ Dept Psychol Beijing 100871 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China

This paper presents a theoretical model and addresses several issues related to life cycle cost analysis to illustrate how time-inconsistent preferences affect consumer choice. The particular case study selects involved consumer choice between a vehicle with high initial acquisition cost but low ownership cost (e.g., an Electric Vehicle, EV) and one with a low initial acquisition cost but high ownership cost (e.g., a conventional Internal Combustion Engine Vehicle, ICEV). To test our theoretical analysis, we conduct an empirical study on how time discounting rates affect consumer choice between ICEVs and EVs with different initial cost ratios. From the survey results, we find that individuals with higher present bias showed irrational purchase behavior even when controlling for wealth level. Specifically, people making some "stronger bias to present' decisions chose higher total cost ICEVs with lower initial cost but higher ownership cost over lower total cost EVs with higher initial cost and lower ownership cost. However, people's long-term discount is not correlated with irrational vehicle purchase behavior. Furthermore, we study the present bias and long-term discount rate in one scenario and found present bias to be correlated with irrational behavior. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Hyperbolic discounting Life-cycle private cost China transport Time discounting Irrational vehicle consumption choice

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