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结构保持嵌入

Structure Preserving Embedding
课程网址: http://videolectures.net/icml09_shaw_spe/  
主讲教师: 结构保持嵌入
开课单位: 哥伦比亚大学
开课时间: 2009-08-26
课程语种: 英语
中文简介:
结构保持嵌入(SPE)是一种用于在欧几里得空间中嵌入图的算法,使得嵌入是低维的,并保持输入图的全局拓扑特性。如果连通性算法(如k近邻)能够在嵌入后仅从节点的坐标中轻松地恢复输入图的边,则保留拓扑。SPE被描述为一个半定程序,它学习由一组线性不等式约束的低秩核矩阵,该线性不等式捕获输入图的连通性结构。传统的图嵌入算法不能根据我们的定义保留结构,因此,结果的可视化可能具有误导性或信息量较小。SPE在图形的可视化和无损压缩方面提供了显著的改进,优于光谱嵌入和拉普拉斯特征图等流行方法。我们发现,许多经典的图和网络只需要几个维度就可以正确嵌入。此外,在降维算法中引入保留结构的约束可以更精确地表示高维数据。
课程简介: Structure Preserving Embedding (SPE) is an algorithm for embedding graphs in Euclidean space such that the embedding is lowdimensional and preserves the global topological properties of the input graph. Topology is preserved if a connectivity algorithm, such as k-nearest neighbors, can easily recover the edges of the input graph from only the coordinates of the nodes after embedding. SPE is formulated as a semidefinite program that learns a low-rank kernel matrix constrained by a set of linear inequalities which captures the connectivity structure of the input graph. Traditional graph embedding algorithms do not preserve structure according to our definition, and thus the resulting visualizations can be misleading or less informative. SPE provides significant improvements in terms of visualization and lossless compression of graphs, outperforming popular methods such as spectral embedding and Laplacian eigenmaps. We find that many classical graphs and networks can be properly embedded using only a few dimensions. Furthermore, introducing structure preserving constraints into dimensionality reduction algorithms produces more accurate representations of highdimensional data.
关 键 词: 结构保持嵌入; 全局拓扑特性; 降维算法
课程来源: 视频讲座网
数据采集: 2022-11-06:chenjy
最后编审: 2022-11-06:chenjy
阅读次数: 38