The Experts below are selected from a list of 120 Experts worldwide ranked by ideXlab platform
Christopher Liaw - One of the best experts on this subject based on the ideXlab platform.
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SPAA - Greedy and Local Ratio Algorithms in the MapReduce Model
Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures, 2018Co-Authors: Nicholas J. A. Harvey, Christopher LiawAbstract:MapReduce has become the de facto standard model for designing distributed algorithms to process big data on a cluster. There has been considerable research on designing efficient MapReduce algorithms for Clustering, Graph optimization, and submodular optimization problems. We develop new techniques for designing greedy and local ratio algorithms in this setting. Our randomized local ratio technique gives $2$-approximations for weighted vertex cover and weighted matching, and an f -approximation for weighted set cover, all in a constant number of MapReduce rounds. Our randomized greedy technique gives algorithms for maximal independent set, maximal clique, and a (1+e)1n Δ-approximation for weighted set cover. We also give greedy algorithms for vertex colouring with $(1+o(1))Δ colours and edge colouring with (1+o(1))Δ colours.
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greedy and local ratio algorithms in the mapreduce model
ACM Symposium on Parallel Algorithms and Architectures, 2018Co-Authors: Nicholas J. A. Harvey, Christopher LiawAbstract:MapReduce has become the de facto standard model for designing distributed algorithms to process big data on a cluster. There has been considerable research on designing efficient MapReduce algorithms for Clustering, Graph optimization, and submodular optimization problems. We develop new techniques for designing greedy and local ratio algorithms in this setting. Our randomized local ratio technique gives $2$-approximations for weighted vertex cover and weighted matching, and an f -approximation for weighted set cover, all in a constant number of MapReduce rounds. Our randomized greedy technique gives algorithms for maximal independent set, maximal clique, and a (1+e)1n Δ-approximation for weighted set cover. We also give greedy algorithms for vertex colouring with $(1+o(1))Δ colours and edge colouring with (1+o(1))Δ colours.
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greedy and local ratio algorithms in the mapreduce model
arXiv: Data Structures and Algorithms, 2018Co-Authors: Nicholas J. A. Harvey, Christopher LiawAbstract:MapReduce has become the de facto standard model for designing distributed algorithms to process big data on a cluster. There has been considerable research on designing efficient MapReduce algorithms for Clustering, Graph optimization, and submodular optimization problems. We develop new techniques for designing greedy and local ratio algorithms in this setting. Our randomized local ratio technique gives $2$-approximations for weighted vertex cover and weighted matching, and an $f$-approximation for weighted set cover, all in a constant number of MapReduce rounds. Our randomized greedy technique gives algorithms for maximal independent set, maximal clique, and a $(1+\epsilon)\ln \Delta$-approximation for weighted set cover. We also give greedy algorithms for vertex colouring with $(1+o(1))\Delta$ colours and edge colouring with $(1+o(1))\Delta$ colours.
Nicholas J. A. Harvey - One of the best experts on this subject based on the ideXlab platform.
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SPAA - Greedy and Local Ratio Algorithms in the MapReduce Model
Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures, 2018Co-Authors: Nicholas J. A. Harvey, Christopher LiawAbstract:MapReduce has become the de facto standard model for designing distributed algorithms to process big data on a cluster. There has been considerable research on designing efficient MapReduce algorithms for Clustering, Graph optimization, and submodular optimization problems. We develop new techniques for designing greedy and local ratio algorithms in this setting. Our randomized local ratio technique gives $2$-approximations for weighted vertex cover and weighted matching, and an f -approximation for weighted set cover, all in a constant number of MapReduce rounds. Our randomized greedy technique gives algorithms for maximal independent set, maximal clique, and a (1+e)1n Δ-approximation for weighted set cover. We also give greedy algorithms for vertex colouring with $(1+o(1))Δ colours and edge colouring with (1+o(1))Δ colours.
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greedy and local ratio algorithms in the mapreduce model
ACM Symposium on Parallel Algorithms and Architectures, 2018Co-Authors: Nicholas J. A. Harvey, Christopher LiawAbstract:MapReduce has become the de facto standard model for designing distributed algorithms to process big data on a cluster. There has been considerable research on designing efficient MapReduce algorithms for Clustering, Graph optimization, and submodular optimization problems. We develop new techniques for designing greedy and local ratio algorithms in this setting. Our randomized local ratio technique gives $2$-approximations for weighted vertex cover and weighted matching, and an f -approximation for weighted set cover, all in a constant number of MapReduce rounds. Our randomized greedy technique gives algorithms for maximal independent set, maximal clique, and a (1+e)1n Δ-approximation for weighted set cover. We also give greedy algorithms for vertex colouring with $(1+o(1))Δ colours and edge colouring with (1+o(1))Δ colours.
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greedy and local ratio algorithms in the mapreduce model
arXiv: Data Structures and Algorithms, 2018Co-Authors: Nicholas J. A. Harvey, Christopher LiawAbstract:MapReduce has become the de facto standard model for designing distributed algorithms to process big data on a cluster. There has been considerable research on designing efficient MapReduce algorithms for Clustering, Graph optimization, and submodular optimization problems. We develop new techniques for designing greedy and local ratio algorithms in this setting. Our randomized local ratio technique gives $2$-approximations for weighted vertex cover and weighted matching, and an $f$-approximation for weighted set cover, all in a constant number of MapReduce rounds. Our randomized greedy technique gives algorithms for maximal independent set, maximal clique, and a $(1+\epsilon)\ln \Delta$-approximation for weighted set cover. We also give greedy algorithms for vertex colouring with $(1+o(1))\Delta$ colours and edge colouring with $(1+o(1))\Delta$ colours.
Hui Xiong - One of the best experts on this subject based on the ideXlab platform.
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semi supervised hierarchical recurrent Graph neural network for city wide parking availability prediction
National Conference on Artificial Intelligence, 2020Co-Authors: Weijia Zhang, Hao Liu, Yanchi Liu, Jingbo Zhou, Hui XiongAbstract:The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting city-wide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical Graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual Graph convolution block and a soft Clustering Graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of \hmgnn outperforms seven state-of-the-art baselines.
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semi supervised hierarchical recurrent Graph neural network for city wide parking availability prediction
arXiv: Learning, 2019Co-Authors: Weijia Zhang, Hao Liu, Yanchi Liu, Jingbo Zhou, Hui XiongAbstract:The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical Graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual Graph convolution block and a soft Clustering Graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of SHARE outperforms seven state-of-the-art baselines.
Erkki Oja - One of the best experts on this subject based on the ideXlab platform.
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Adaptive multiplicative updates for quadratic nonnegative matrix factorization
Neurocomputing, 2014Co-Authors: He Zhang, Zhirong Yang, Erkki OjaAbstract:In Nonnegative Matrix Factorization (NMF), a nonnegative matrix is approximated by a product of lower-rank factorizing matrices. Quadratic Nonnegative Matrix Factorization (QNMF) is a new class of NMF methods where some factorizing matrices occur twice in the approximation. QNMF finds its applications in Graph partition, bi-Clustering, Graph matching, etc. However, the original QNMF algorithms employ constant multiplicative update rules and thus have mediocre convergence speed. Here we propose an adaptive multiplicative algorithm for QNMF which is not only theoretically convergent but also significantly faster than the original implementation. An adaptive exponent scheme has been adopted for our method instead of the old constant ones, which enables larger learning steps for improved efficiency. The proposed method is general and thus can be applied to QNMF with a variety of factorization forms and with the most commonly used approximation error measures. We have performed extensive experiments, where the results demonstrate that the new method is effective in various QNMF applications on both synthetic and real-world datasets.
Weijia Zhang - One of the best experts on this subject based on the ideXlab platform.
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semi supervised hierarchical recurrent Graph neural network for city wide parking availability prediction
National Conference on Artificial Intelligence, 2020Co-Authors: Weijia Zhang, Hao Liu, Yanchi Liu, Jingbo Zhou, Hui XiongAbstract:The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting city-wide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical Graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual Graph convolution block and a soft Clustering Graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of \hmgnn outperforms seven state-of-the-art baselines.
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semi supervised hierarchical recurrent Graph neural network for city wide parking availability prediction
arXiv: Learning, 2019Co-Authors: Weijia Zhang, Hao Liu, Yanchi Liu, Jingbo Zhou, Hui XiongAbstract:The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical Graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual Graph convolution block and a soft Clustering Graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of SHARE outperforms seven state-of-the-art baselines.