graph embedding

The graph surface embedding must have more important applications in many fields.

图的曲面嵌入在很多领域必将有更多的重要应用。

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In this paper, We obtain the total genus polynomials for two new classes of4-regular graphs by using the joint tree model of a graph embedding introduced by Yanpei Liu.

本文利用刘彦佩提出的嵌入的联树模型，得出了两类新的四正则图的完全亏格多项式，并推导出已有结果的两类图的完全亏格多项式。

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Book embedding of arc graph is just a concept since it was proposed in 1999 because of its high time complexity ( NP-complete).

RNA二级结构图的书嵌入自1999年提出以来由于其高时间复杂性（NP完全）而一直停留在概念上。

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In this thesis, a general framework for matrix-based feature extraction algorithms is proposed from the point of view of graph embedding.

我们从图嵌入的角度出发，提出了一个基于矩阵的特征提取方法的一般理论框架。

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There are two fields in topological graph theory: one is the study of the properties of graph embedding.

本文研究属第一个方面，即研究图的嵌入的最大亏格问题。

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These issues mainly include novel subspace analysis methods derived from the idea of manifold learning, the theoretical framework of graph embedding for matrix-based feature extraction algorithms with their applications to face and palmprint recognition.

本文对人脸与掌纹识别的子空间特征提取方法进行了深入研究，主要包括流形学习导出的新子空间方法、基于矩阵的特征提取方法的图嵌入理论框架及其在人脸和掌纹识别中的应用。

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Using the graph surface embedding method, we have solved the routing problem in the wireless sensor network.

我们使用图的曲面嵌入这种整体几何的方法来解决无线传感器网络中的路由问题。

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Based on the out-of-sample framework and the graph embedding framework, the thesis presents a more general common framework for manifold learning. The new framework embodies graph construction, tensor learning, incremental learning and supervised learning.

提出新的流形学习统一框架在样本泛化与图嵌理论的基础上，提出了更一般的流形学习统一框架，这一框架包含了图的建立、张量学习、增量学习和有监督学习。

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This is the problem of embedding genus distribution for a graph.

这就是图的嵌入亏格分布问题。

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Dimensionality Reduction based on the combination of Graph Embedding and MMI

基于图嵌入和最大互信息组合的降维

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A new graph embedding and similarity measurement method is proposed in this thesis based on complex network theory.

在传统的图像的结构图表示方法的基础上，结合复杂网络相关理论，提出了一种基于复杂网络模型的结构图特征提取与相似性度量方法。

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The applications in wireless sensor network using Graph surface embedding For the planar graph formed by wireless sensor network, we assign it a Euclidean metric and embed it in the complex plane, by optimizing a Mobius transformation, we can map the graph onto the sphere.

图的曲面嵌入在无线传感器网络上的应用对于由无线传感器网络所形成的平面图，赋予欧氏度量，在复平面通过优化一个莫比乌斯变换，并将平面图嵌入到上半球面。

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Book embedding classification of RE arc graph is realized.

实现了RE弧图的书嵌入分类。

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A new feature-based resolution-robust feature representation algorithm was proposed, namely a graph embedding algorithm ( FisherNPE) based on resolution level difference probabilistic similarity measure, which aims to improve the limitation of resolution-robust feature representation.

提出一种基于特征的分辨率稳健特征表达算法：基于分辨率级差概率准则的图嵌入算法（FisherNPE），致力于解决分辨率稳健特征表达能力的局限性问题。

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We employed the topological method to get a relatively smaller genus of graph embedding surface, and it is a quasi-planar.

我们使用拓扑的方法得到图的较小亏格的拓扑曲面，并且该图的拓扑曲面是拟平面图。

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Therefore, the existence of bijective correspondence between graph embedding and graph rotation enables us to replace graph operations with the operations of graph rotation system, which can guarantee the 2-manifold property of the graph embedding.

由于图形旋转系统和图形嵌入之间存在着&对应的关系，因此我们可以把对图形的操作转换为对旋转系统的操作，从而保证了图形体在经过拓扑变换后依然能够保证其二维流形性。

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The theory of graph rotation system topologically represents the graph embedding.

图形旋转系统理论是拓扑意义上对图形体的数据表达。

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Inspired by the methods in algebraic topology, we invented an algorithm, which can efficiently reduce the genus, and we can find a quasi-planar graph embedding.

受到代数拓扑的启发，我们发明了一个算法，可以高效单调的减少图嵌入曲面的亏格，并可寻找到称为拟平面图的拓扑曲面。

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