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

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 prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,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.

The most tenuous group query

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 formation.However,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 knowledge.Thus,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 group.To 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 strategies.Since 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 degree.For 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 considered.The experimental results on real datasets manifest that the proposed algorithms outperform existing approaches on both efficiency and group tenuity.

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

In smart phones,vehicles and wearable devices,GPS sensors are ubiquitous and collect a lot of valuable spatial data from the real world.Given 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 monitoring.Most 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 inefficient.In this paper,we propose a novel GPU-accelerated algorithm,namely,GAM,to tackle MaxRS queries in road networks in two phases efficiently.In 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 checking.In 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 cells.Finally,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.