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A sparse algorithm for adaptive pruning least square support vector regression machine based on global representative point ranking

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Journal of Systems Engineering and Electronics 2021年第1期32卷 151-162页

作者： HU Lei YI Guoxing HUANG Chao School of Astronautics Harbin Institute of TechnologyHarbin 150001China

Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast *** this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is *** first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data ***,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the *** removed data points are utilized to test the temporary learning model which ensures the regression ***,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.

关键词： least square support vector regression(LSSVR) global representative point ranking(GRPR) initial training dataset pruning strategy sparsity regression accuracy

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GAM:A GPU-Accelerated Algorithm for MaxRS Queries in Road Networks

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Journal of Computer Science & Technology 2022年第5期37卷 1005-1025页

作者： Jian Chen Kai-Qi Zhang Tian Ren Zhen-Qing Wu Hong Gao School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

In smart phones,vehicles and wearable devices,GPS sensors are ubiquitous and collect a lot of valuable spatial data from the real *** a set of weighted points and a rectangle r in the space,a maximizing range sum(MaxRS)query is to find the position of r,so as to maximize the total weight of the points covered by r(i.e.,the range sum).It has a wide spectrum of applications in spatial crowdsourcing,facility location and traffic *** of the existing research focuses on the Euclidean space;however,in real life,the user’s moving route is constrained by the road network,and the existing MaxRS query algorithms in the road network are *** this paper,we propose a novel GPU-accelerated algorithm,namely,GAM,to tackle MaxRS queries in road networks in two phases *** phase 1,we partition the entire road network into many small cells by a grid and theoretically prove the correctness of parallel query results by grid shifting,and then we propose an effective multi-grained pruning technique,by which the majority of cells can be pruned without further *** phase 2,we design a GPU-friendly storage structure,cell-based road network(CRN),and a two-level parallel framework to compute the final result in the remaining ***,we conduct extensive experiments on two real-world road networks,and the experimental results demonstrate that GAM is on average one order faster than state-of-the-art competitors,and the maximum speedup can achieve about 55 times.

关键词： road network maximizing range sum GPU acceleration pruning strategy

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The most tenuous group query

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Frontiers of Computer Science 2023年第2期17卷 197-208页

作者： Na LI Huaijie ZHU Wenhao LU Ningning CUI Wei LIU Jian YIN Jianliang XU Wang-Chien LEE School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510006China Laboratory of Big Data Analysis and Processing Guangzhou 510006China School of Artificial Intelligence Sun Yat-Sen UniversityGuangzhou 510006China Department of Computer Science Anhui UniversityHefei 230601China Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China Department of Computer Science The Pennsylvania State UniversityState College 19019USA

Rtecently a lot of works have been investigating to find the tenuous groups,i.e.,groups with few social interactions and weak relationships among members,for reviewer selection and psycho-educational group ***,the metrics(e.g.,k-triangle,k-line,and k-tenuity)used to measure the tenuity,require a suitable k value to be specified which is difficult for users without background ***,in this paper we formulate the most tenuous group(MTG)query in terms of the group distance and average group distance of a group measuring the tenuity to eliminate the influence of parameter k on the tenuity of the *** address the MTG problem,we first propose an exact algorithm,namely MTGVDIS,which takes priority to selecting those vertices whose vertex distance is large,to generate the result group,and also utilizes effective filtering and pruning *** MTGVDIS is not fast enough,we design an efficient exact algorithm,called MTG-VDGE,which exploits the degree metric to sort the vertexes and proposes a new combination order,namely degree and reverse based branch and bound(DRBB).MTG-VDGE gives priority to those vertices with small *** a large p,we further develop an approximation algorithm,namely MTG-VDLT,which discards candidate attendees with high degree to reduce the number of vertices to be *** experimental results on real datasets manifest that the proposed algorithms outperform existing approaches on both efficiency and group tenuity.

关键词： tenuous group pruning strategy social network group query

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Towards Effective Author Name Disambiguation by Hybrid Attention

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Journal of Computer Science & Technology 2024年第4期39卷 929-950页

作者： Qian Zhou Wei Chen Peng-Peng Zhao An Liu Jia-Jie Xu Jian-Feng Qu Lei Zhao School of Computer Science and Technology Soochow UniversitySuzhou 215006China

Author name disambiguation(AND)is a central task in academic search,which has received more attention recently accompanied by the increase of authors and academic *** tackle the AND problem,existing studies have proposed various approaches based on different types of information,such as raw document features(e.g.,co-authors,titles,and keywords),the fusion feature(e.g.,a hybrid publication embedding based on multiple raw document features),the local structural information(e.g.,a publication's neighborhood information on a graph),and the global structural information(e.g.,interactive information between a node and others on a graph).However,there has been no work taking all the above-mentioned information into account and taking full advantage of the contributions of each raw document feature for the AND problem so *** fill the gap,we propose a novel framework named EAND(Towards Effective Author Name Disambiguation by Hybrid Attention).Specifically,we design a novel feature extraction model,which consists of three hybrid attention mechanism layers,to extract key information from the global structural information and the local structural information that are generated from six similarity graphs constructed based on different similarity coefficients,raw document features,and the fusion *** hybrid attention mechanism layer contains three key modules:a local structural perception,a global structural perception,and a feature ***,the mean absolute error function in the joint loss function is used to introduce the structural information loss of the vector *** results on two real-world datasets demonstrate that EAND achieves superior performance,outperforming state-of-the-art methods by at least+2.74%in terms of the micro-F1 score and+3.31%in terms of the macro-F1 score.

关键词： author name disambiguation multiple-feature information hybrid attention pruning strategy structural information loss of vector space

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