The Experts below are selected from a list of 125859 Experts worldwide ranked by ideXlab platform
Jianxin Wang - One of the best experts on this subject based on the ideXlab platform.
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small Candidate Set for translational pattern search
International Symposium on Algorithms and Computation, 2019Co-Authors: Ziyun Huang, Qilong Feng, Jianxin WangAbstract:In this paper, we study the following pattern search problem: Given a pair of point Sets A and B in fixed dimensional space R^d, with |B| = n, |A| = m and n >= m, the pattern search problem is to find the translations T's of A such that each of the identified translations induces a matching between T(A) and a subSet B' of B with cost no more than some given threshold, where the cost is defined as the minimum bipartite matching cost of T(A) and B'. We present a novel algorithm to produce a small Set of Candidate translations for the pattern search problem. For any B' subSeteq B with |B'| = |A|, there exists at least one translation T in the Candidate Set such that the minimum bipartite matching cost between T(A) and B' is no larger than (1+epsilon) times the minimum bipartite matching cost between A and B' under any translation (i.e., the optimal translational matching cost). We also show that there exists an alternative solution to this problem, which constructs a Candidate Set of size O(n log^2 n) in O(n log^2 n) time with high probability of success. As a by-product of our construction, we obtain a weak epsilon-net for hypercube ranges, which significantly improves the construction time and the size of the Candidate Set. Our technique can be applied to a number of applications, including the translational pattern matching problem.
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ISAAC - Small Candidate Set for translational pattern search
2019Co-Authors: Ziyun Huang, Qilong Feng, Jianxin WangAbstract:In this paper, we study the following pattern search problem: Given a pair of point Sets A and B in fixed dimensional space R^d, with |B| = n, |A| = m and n >= m, the pattern search problem is to find the translations T's of A such that each of the identified translations induces a matching between T(A) and a subSet B' of B with cost no more than some given threshold, where the cost is defined as the minimum bipartite matching cost of T(A) and B'. We present a novel algorithm to produce a small Set of Candidate translations for the pattern search problem. For any B' subSeteq B with |B'| = |A|, there exists at least one translation T in the Candidate Set such that the minimum bipartite matching cost between T(A) and B' is no larger than (1+epsilon) times the minimum bipartite matching cost between A and B' under any translation (i.e., the optimal translational matching cost). We also show that there exists an alternative solution to this problem, which constructs a Candidate Set of size O(n log^2 n) in O(n log^2 n) time with high probability of success. As a by-product of our construction, we obtain a weak epsilon-net for hypercube ranges, which significantly improves the construction time and the size of the Candidate Set. Our technique can be applied to a number of applications, including the translational pattern matching problem.
Ziyun Huang - One of the best experts on this subject based on the ideXlab platform.
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small Candidate Set for translational pattern search
International Symposium on Algorithms and Computation, 2019Co-Authors: Ziyun Huang, Qilong Feng, Jianxin WangAbstract:In this paper, we study the following pattern search problem: Given a pair of point Sets A and B in fixed dimensional space R^d, with |B| = n, |A| = m and n >= m, the pattern search problem is to find the translations T's of A such that each of the identified translations induces a matching between T(A) and a subSet B' of B with cost no more than some given threshold, where the cost is defined as the minimum bipartite matching cost of T(A) and B'. We present a novel algorithm to produce a small Set of Candidate translations for the pattern search problem. For any B' subSeteq B with |B'| = |A|, there exists at least one translation T in the Candidate Set such that the minimum bipartite matching cost between T(A) and B' is no larger than (1+epsilon) times the minimum bipartite matching cost between A and B' under any translation (i.e., the optimal translational matching cost). We also show that there exists an alternative solution to this problem, which constructs a Candidate Set of size O(n log^2 n) in O(n log^2 n) time with high probability of success. As a by-product of our construction, we obtain a weak epsilon-net for hypercube ranges, which significantly improves the construction time and the size of the Candidate Set. Our technique can be applied to a number of applications, including the translational pattern matching problem.
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ISAAC - Small Candidate Set for translational pattern search
2019Co-Authors: Ziyun Huang, Qilong Feng, Jianxin WangAbstract:In this paper, we study the following pattern search problem: Given a pair of point Sets A and B in fixed dimensional space R^d, with |B| = n, |A| = m and n >= m, the pattern search problem is to find the translations T's of A such that each of the identified translations induces a matching between T(A) and a subSet B' of B with cost no more than some given threshold, where the cost is defined as the minimum bipartite matching cost of T(A) and B'. We present a novel algorithm to produce a small Set of Candidate translations for the pattern search problem. For any B' subSeteq B with |B'| = |A|, there exists at least one translation T in the Candidate Set such that the minimum bipartite matching cost between T(A) and B' is no larger than (1+epsilon) times the minimum bipartite matching cost between A and B' under any translation (i.e., the optimal translational matching cost). We also show that there exists an alternative solution to this problem, which constructs a Candidate Set of size O(n log^2 n) in O(n log^2 n) time with high probability of success. As a by-product of our construction, we obtain a weak epsilon-net for hypercube ranges, which significantly improves the construction time and the size of the Candidate Set. Our technique can be applied to a number of applications, including the translational pattern matching problem.
Wei Liu - One of the best experts on this subject based on the ideXlab platform.
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ICSI (1) - A novel simple Candidate Set method for symmetric TSP and its application in MAX-MIN ant system
Lecture Notes in Computer Science, 2012Co-Authors: Deng Miao, Jihong Zhang, Yongsheng Liang, Guangming Lin, Wei LiuAbstract:Traveling Salesman Problem (TSP) is a kind of typical NP problem and has been extensively researched in combinatorial optimization. For solving it more effectively, Candidate Set is used in many algorithms in order to limit the selecting range when choosing next city to move, such as in Ant Systems, or to initialize a local optimum solution, such as in Lin-Kernighan Heuristic (LKH) algorithm. A novel simple method for generating Candidate Set is proposed in this paper and applied into MAX-MIN Ant System (MMAS) for symmetric TSP problem. Experimental results show that it has better performance than other Ant Systems including MMAS. Moreover, this method can be used in other algorithms for symmetric TSP problem.
H M Lloyd - One of the best experts on this subject based on the ideXlab platform.
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vectorized Candidate Set selection for parallel ant colony optimization
Genetic and Evolutionary Computation Conference, 2018Co-Authors: Joshua Peake, Martyn Amos, Paraskevas Yiapanis, H M LloydAbstract:Ant Colony Optimization (ACO) is a well-established nature-inspired heuristic, and parallel versions of the algorithm now exist to take advantage of emerging high-performance computing processors. However, careful attention must be paid to parallel components of such implementations if the full benefit of these platforms is to be obtained. One such component of the ACO algorithm is next node selection, which presents unique challenges in a parallel Setting. In this paper, we present a new node selection method for ACO, Vectorized Candidate Set Selection (VCSS), which achieves significant speedup over existing selection methods on a test Set of Traveling Salesman Problem instances.
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GECCO (Companion) - Vectorized Candidate Set selection for parallel ant colony optimization
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018Co-Authors: Joshua Peake, Martyn Amos, Paraskevas Yiapanis, H M LloydAbstract:Ant Colony Optimization (ACO) is a well-established nature-inspired heuristic, and parallel versions of the algorithm now exist to take advantage of emerging high-performance computing processors. However, careful attention must be paid to parallel components of such implementations if the full benefit of these platforms is to be obtained. One such component of the ACO algorithm is next node selection, which presents unique challenges in a parallel Setting. In this paper, we present a new node selection method for ACO, Vectorized Candidate Set Selection (VCSS), which achieves significant speedup over existing selection methods on a test Set of Traveling Salesman Problem instances.
Pavel Zezula - One of the best experts on this subject based on the ideXlab platform.
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PPP-Codes for Large-Scale Similarity Searching
Transactions on Large-Scale Data- and Knowledge-Centered Systems XXIV, 2016Co-Authors: David Novak, Pavel ZezulaAbstract:Many current applications need to organize data with respect to mutual similarity between data objects. Ai??typical general strategy to retrieve objects similar to a given sample is to access and then refine a Candidate Set of objects. We propose an indexing and search technique that can significantly reduce the Candidate Set size by combination of several space partitionings. Specifically, we propose a mapping of objects from a generic metric space onto main memory codes using several pivot spaces; our search algorithm first ranks objects within each pivot space and then aggregates these rankings producing a Candidate Set reduced by two orders of magnitude while keeping the same answer quality. Our approach is designed to well exploit contemporaryi??HW: 1i??larger main memories allow us to use rich and fast index, 2i??multi-core CPUs well suit our parallel search algorithm, and 3i??SSD disks without mechanical seeks enable efficient selective retrieval of Candidate objects. The gain of the significant Candidate Set reduction is paid by the overhead of the Candidate ranking algorithm and thus our approach is more advantageous for dataSets with expensive Candidate Set refinement, i.e. large data objects or expensive similarity function. On real-life dataSets, the search time speedup achieved by our approach is by factor of two to five.
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DEXA (2) - Rank Aggregation of Candidate Sets for Efficient Similarity Search
Lecture Notes in Computer Science, 2014Co-Authors: David Novak, Pavel ZezulaAbstract:Many current applications need to organize data with respect to mutual similarity between data objects. Generic similarity retrieval in large data collections is a tough task that has been drawing researchers’ attention for two decades. A typical general strategy to retrieve the most similar objects to a given example is to access and then refine a Candidate Set of objects; the overall search costs (and search time) then typically correlate with the Candidate Set size. We propose a generic approach that combines several independent indexes by aggregating their Candidate Sets in such a way that the resulting Candidate Set can be one or two orders of magnitude smaller (while keeping the answer quality). This achievement comes at the expense of higher computational costs of the ranking algorithm but experiments on two real-life and one artificial dataSets indicate that the overall gain can be significant.
