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Adjacency list

About: Adjacency list is a research topic. Over the lifetime, 4419 publications have been published within this topic receiving 78449 citations.


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Journal ArticleDOI
TL;DR: It is shown that results of SA search are similar to those of GP in both the efficiency of the learned classifiers and in its linguistic interpretability, while the memory consumption of the learning process is lower.

145 citations

Journal ArticleDOI
TL;DR: The influence of the adjacency effects on determination of the abundance of chlorophyll in water is studied by combining use of the red and the infrared bands for aerosol remote sensing and the blue/green-ratio technique for retrieval of these data.
Abstract: The making of atmospheric corrections is a critical task in the interpretation of ocean color imagery. In coastal areas, a fraction of the light reflected by the land reaches a sensor. Modeling the reduction of image contrast when the atmospheric turbidity increases, the so-called adjacency effect, requires large amounts of computing time. To model this effect we developed a simple approach based on the primary scattering approximation for both nadir and off-nadir views. A sensitivity study indicates that the decisive criterion for measurement accuracy for aerosols is their vertical distribution. As this distribution cannot generally be determined from space, it is not possible to include a suitable correction of the adjacency effects on satellite imagery. Conversely, we propose a simple correction for molecular scattering based on the isotropic approximation. We also address the problem of reduction of the coupling between the Fresnel reflection and the atmosphere for observations of coastal water. We study the influence of the adjacency effects on determination of the abundance of chlorophyll in water by combining use of the red and the infrared bands for aerosol remote sensing and the blue/green-ratio technique for retrieval of these data.

144 citations

Journal ArticleDOI
TL;DR: Almost tight lower and upper bounds for the bounded error quantum query complexity of Connectivity, StrongConnectivity, Minimum Spanning Tree, and Single Source Shortest Paths are given.
Abstract: Quantum algorithms for graph problems are considered, both in the adjacency matrix model and in an adjacency list-like array model. We give almost tight lower and upper bounds for the bounded error quantum query complexity of Connectivity, Strong Connectivity, Minimum Spanning Tree, and Single Source Shortest Paths. For example, we show that the query complexity of Minimum Spanning Tree is in $\Theta(n^{3/2})$ in the matrix model and in $\Theta(\sqrt{nm})$ in the array model, while the complexity of Connectivity is also in $\Theta(n^{3/2})$ in the matrix model but in $\Theta(n)$ in the array model. The upper bounds utilize search procedures for finding minima of functions under various conditions.

143 citations

Journal ArticleDOI
TL;DR: The k^2-tree is presented, a novel Web graph representation based on a compact tree structure that takes advantage of large empty areas of the adjacency matrix of the graph and offers the least space usage while supporting fast navigation to predecessors and successors and sharply outperforming the others on the extended queries.

139 citations

Journal ArticleDOI
TL;DR: This work proposes to learn a low-rank kernel matrix which exploits the similarity nature of the kernel matrix and seeks an optimal kernel from the neighborhood of candidate kernels.
Abstract: Constructing the adjacency graph is fundamental to graph-based clustering. Graph learning in kernel space has shown impressive performance on a number of benchmark data sets. However, its performance is largely determined by the chosen kernel matrix. To address this issue, previous multiple kernel learning algorithm has been applied to learn an optimal kernel from a group of predefined kernels. This approach might be sensitive to noise and limits the representation ability of the consensus kernel. In contrast to existing methods, we propose to learn a low-rank kernel matrix which exploits the similarity nature of the kernel matrix and seeks an optimal kernel from the neighborhood of candidate kernels. By formulating graph construction and kernel learning in a unified framework, the graph and consensus kernel can be iteratively enhanced by each other. Extensive experimental results validate the efficacy of the proposed method.

137 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023209
2022439
2021283
2020280
2019296
2018232