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Classification Based on Multilayer Extreme Learning Machine for Motor Imagery Task from EEG Signals

Procedia Computer Science 2016年 88卷 176-184页

作者： Lijuan Duan Menghu Bao Jun Miao Yanhui Xu Juncheng Chen College of Computer Science and Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing 100124 China

Classification of motor imagery electroencephalogram (EEG) is one of the most important technologies for BCI. To improve the accuracy, this paper introduces a classification system based on Multilayer Extreme Learning Machine (ML-ELM). In the system, the combination of PCA and LDA is chosen as the method of feature extraction and the ML-ELM is used to classify. The ML-ELM has not only the advantage which ELM has but also better performance than ELM. In the experiment, our method is compared with the methods based on ELM, such as kernel-ELM, Constrained-ELM and V-ELM, and some state-of–the-art methods on the same dataset. The experimental results show that ML-ELM is much more suitable for motor imagery EEG data and has better performance than the others.

关键词： Electroencephalogram Classification Motor Imagery Extreme Learning Machine Multilayer Extreme Learning Machine

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Constraint Free Preference Preserving Hashing for Fast Recommendation

Constraint Free Preference Preserving Hashing for Fast Recom...

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IEEE Global Communications Conference

作者： Yan Zhang Guowu Yang Defu Lian Hong Wen Jinsong Wu Center for Cyber Security University of Electronic Science and Technology of China Big Data Research Center University of Electronic Science and Technology of China National Key Lab of Commun. University of Electronic Science and Technology of China Department of Electrical Engineering Universidad de Chile

ISBN: (纸本)9781509013296

Recommender systems have been widely used to deal with information overload, by suggesting relevant items that match users' personal interest. One of the most popular recommendation techniques is matrix factorization (MF). The inner products of learned latent factors between users and items can estimate users' preferences for items with high accuracy, but the preferences ranking is time consuming. Thus, hashing-based fast search technologies were exploited in recommender systems. However, most previous approaches consist of two stages: continuous latent factor learning and binary quantization, but they didn't well deal with the change of inner product arising from quantization. To this end, in this paper, we propose a constraint free preference preserving hashing method, which quantizes both norm and similarity in dot product. We also design an algorithm to optimize the bit length for norm quantization. The performance of our method is evaluated on three real world datasets. The results confirm that the proposed model can improve recommendation performance by 11%-15%, as compared with the state-of-the-art hashing approaches.

关键词： Recommender system Preference ranking Nor-m quantization Optimal bit Recommender systems Dot product Quantization hashing Preferences User preferences preference Consumers

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Heuristic research on a resource constrained project scheduling problem

Heuristic research on a resource constrained project schedul...

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Proceedings of the Asia-Pacific Conference on Computer Science and Applications, CSAC 2014

作者： Wei, Zhengsheng Li, Haohua Li, Mingwei Dong, Xingye Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and IT Beijing Jiaotong University Beijing China Systems Engineering Research Institute Beijing China

ISBN: (纸本)9781138028111

According to the production requirements of certain enterprises, a special Resourceconstrained Project Scheduling Problem (RCPSP) is proposed based on the classical RCPSP. The constraints of site for jobs and the constraints among different renewable resources are added. A constructive heuristic algorithm is presented, in which three heuristic rules are used. For the site selection, the one available earlier and most operations can be scheduled on is superior. For the assignment of renewable resources, the earliest available and the most capable ones are preferred. For the choice of the operation, the rule of smallest completion time is applied. The method to solve possible deadlock during the construction is also designed. Experimental results on three practical instances show that the proposed algorithm can construct solutions in negligible time, saving a lot of human costs and achieve better solutions. © 2015 Taylor & Francis Group, London.

关键词： Site selection

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A novel method for road centerline extraction from high spatial resolution satellite images

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Journal of Information Hiding and Multimedia Signal Processing 2016年第5期7卷 1069-1080页

作者： Xu, Rui Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Spatial Information Research Center of Fujian Province Fuzhou University No.523 Gongye Road Fuzhou350002 China College of Information Science and Engineering Fujian University of Technology No.3 Xueyuan Road University Town Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Fuzhou350108 China

An integrated method for road centerline extraction has been proposed in this paper which consists of four main steps. First, the Graph Cut algorithm using proposed similarity function is employed to segment the image, and the segmented image is thresholded to initial binary skeleton. Second, the road skeleton is refined by using a series of morphology operations and shape features. Third, the curvilinear feature of road skeleton is detected by a set of multiple filtering detectors. Finally,a regression method is performed to extract smooth and accurate road centerlines. Validation tests show that the proposed method is able to achieve a comparatively good performance in road centerline extraction from high spatial resolution satellite images. © 2016, Ubiquitous International. All Rights Reserved.

关键词： Regression analysis

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A hierarchical object-based method for suburban road extraction from remote sensing image

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ICIC Express Letters, Part B: Applications 2016年第12期7卷 2671-2676页

作者： Xu, Rui College of Information Science and Engineering Fujian University of Technology No. 3 Xueyuan Road University Town Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road University Town Fuzhou350118 China Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education Spatial Information Research Center of Fujian Province Fuzhou University No. 523 Gongye Road Fuzhou350002 China

Road extraction from remote sensing image is a very popular topic. There are many methods dealing with this problem, but few of them focus on suburban roads. This paper presents a hierarchical object-based method for suburban road extraction. First, the image is preprocessed by the Normalized Difference Vegetation Index (NDVI) and the Hue-Saturation-Intensity (HSI) Index. Vegetation regions, shadows and water areas are then removed. Second, texture features are applied to classifying the preprocessed image into two categories: road and non-road. Homogeneous property is combined to remove false classification so as to generate initial road skeleton. Third, the road skeleton is refined by using a series of morphology operations and shape features. Fourth, the curvilinear feature of road skeleton is detected by a set of multiple filtering detectors. Finally, a regression method is performed to extract smooth and accurate road centerlines. The experimental results indicate that the proposed method is suitable for suburban road extraction. © 2016 ISSN 2185-2766.

关键词： Vegetation

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A multistage method for road extraction from optical remotely sensed imagery

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Journal of Information Hiding and Multimedia Signal Processing 2016年第2期7卷 438-447页

作者： Xu, Rui Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education Spatial Information Research Center of Fujian Province Fuzhou University No.523 Gongye Road Fuzhou350002 China College of Information Science and Engineering Fujian University of Technology No.3 Xueyuan Road University Town Fuzhou350108 China

This paper presents a multistage framework for road extraction from remotely sensed imagery. The framework is a combination of multifeature-based mean shift, SVM classifier, and shape feature filter. Mean shift is first employed to cluster the remotely sensed images in the fusion of spectral-texture features and then a classifier of SVM is applied to classify the segmented images into two classes: the road class and the non-road class. Finally, the road class is refined by using road shape features. The experimental results show that this method is efficient in extracting road from remotely sensed imary. © 2016.

关键词： Classification (of information)

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Robust latent poisson deconvolution from multiple imperfect features for web topic detection

Robust latent poisson deconvolution from multiple imperfect ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Fei Tao Junbiao Pang Chunjie Zhang Liang Li Li Su Weigang Zhang Qingming Huang Guiping Su School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China College of Metropolitan Transportation Beijing University of Technology China Key Lab on Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China School of Computer Science and Technology Harbin Institute of Technology China

ISBN: (纸本)9781467372596

In web topic detection, detecting “hot” topics from enormous User-Generated Content (UGC) on web data poses two main difficulties that conventional approaches can barely handle: 1) poor feature representations from noisy images and short texts; and 2) uncertain roles of modalities where visual content is either highly or weakly relevant to textual cues due to less-constrained data. In this paper, following the detection by ranking approach, we address the problem by learning a robust shared representation from multiple, noisy and complementary features, and integrating both textual and visual graphs into a k-Nearest Neighbor Similarity Graph (k-N 2 SG). Then Non-negative Matrix Factorization using Random walk (NMFR) is introduced to generate topic candidates. An efficient fusion of multiple graphs is then done by a Latent Poisson Deconvolution (LPD) which consists of a poisson deconvolution with sparse basis similarities for each edge. Experiments show significantly improved accuracy of the proposed approach in comparison with the state-of-the-art methods on two public data sets.

关键词： Visualization Deconvolution Robustness Feature extraction Optimization Correlation Noise measurement

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Evaluating journals' yearly impact with altmetric indicators

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Chinese Journal of Library and Information Science 2015年第2期 25-38页

作者： Simon S.LI Fred Y.YE School of Information Management Nanjing University Jiangsu Key Lab of Data Engineering & Knowledge Service Nanjing University

Purpose： Applying SSCI journals of library and information science （LIS） as the research sample, we explore the feasibility of measuring academic journals＇ yearly social impact by using altmetric indicators. Design/methodology/approach： Using a sample of 66 SSCI joumals in LIS published in 2013, statistics regarding journal mentions in social media and other online tools were retrieved from *** and meanwhile citation data was also collected from JCR and Scopus. Based on the method of principal component analysis, data was analyzed for associations between the altmetric and traditional metrics to demonstrate the effect ofaltmetric indicators on measuring academic j oumals＇ yearly impact. Findings： The Spearman＇s rank correlation test results show that altmetric indicators and traditional citation counts were significantly correlated, indicating that altmetrics can be used to measure a journal＇s yearly social impact. Research limitations： The time frame of data collected from *** may not be consistent with that of JCR and Scopus citation data. Practical implications： A new method is provided based on altmetrics for evaluating the social impact of academic journals, which can be applied to design new indicators of short-term journal impact. Originality value： In this paper, we have established a method for evaluating the social impact of academic journals based on altmetric indictors. Altmetrics can be complementary to traditional citation metrics in assessing a journal＇s impact within a year or even in a shorter period of time.

关键词： Altmetrics Journal evaluation Informetrics Bibliometrics Principalcomponent analysis （PCA）

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Competitive hopfield neural network with chaotic dynamics for partitional clustering problem

Competitive hopfield neural network with chaotic dynamics fo...

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International Conference on Services Systems and Services Management, ICSSSM

作者： Gang Yang Junyan Yi Jieping Xu Xirong Li Key Lab of Data Engineering and Knowledge Engineering Renmin University of China China Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (纸本)9781479983292

In this paper, an algorithm, named CCHN, is proposed to solve the partitional clustering problem. An outer chaotic mechanism with annealing strategy is introduced into the competitive Hopfield neural network to construct CCHN for expecting better opportunities of converging to the optimal solution. In addition to retain the competitive characteristics of the conventional competitive Hopfield neural network, CCHN displays a rich range of complex and flexible chaotic dynamics. The chaotic dynamics and the annealing strategy guarantee the powerful searching ability and the effective convergence of CCHN. Results simulated on clustering benchmark problems show that CCHN algorithm is more likely to find an optimal or near-optimal solution with a higher successful ratio than previous algorithms.

关键词： Clustering algorithms Partitioning algorithms Neurons Heuristic algorithms Convergence Neural networks Benchmark testing

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Modeling parameter interactions in ranking SVM 15

Modeling parameter interactions in ranking SVM

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24th ACM International Conference on Information and Knowledge Management, CIKM 2015

作者： Zhang, Yaogong Xu, Jun Lan, Yanyan Guo, Jiafeng Xie, Maoqiang Huang, Yalou Cheng, Xueqi College of Computer and Control Engineering Nankai University China CAS Key Lab. of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China College of Software Nankai University China

ISBN: (纸本)9781450337946

Ranking SVM, which formalizes the problem of learning a ranking model as that of learning a binary SVM on preference pairs of documents, is a state-of-the-art ranking model in information retrieval. The dual form solution of Ranking SVM model can be written as a linear combination of the preference pairs, i.e., w = Σ(i,j)αij(xi - xj), where αij denotes the Lagrange parameters associated with each pair (i,j). It is obvious that there exist significant interactions over the document pairs because two preference pairs could share a same document as their items. Thus it is natural to ask if there also exist interactions over the model parameters αij, which we may leverage to propose better ranking model. This paper aims to answer the question. Firstly, we found that there exists a low-rank structure over the Ranking SVM model parameters αij, which indicates that the interactions do exist. Then, based on the discovery, we made a modification on the original Ranking SVM model by explicitly applying a low-rank constraint to the parameters. Specifically, each parameter αij is decomposed as a product of two low-dimensional vectors, i.e., αij = (υi,υj), where vectors vi and vj correspond to document i and j, respectively. The learning process, thus, becomes to optimize the modified dual form objective function with respect to the low-dimensional vectors. Experimental results on three LETOR datasets show that our method, referred to as Factorized Ranking SVM, can outperform state-of-the-art baselines including the conventional Ranking SVM. © 2015 ACM.

关键词： Information retrieval

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