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用于分类的距离图的快速构造

A Fast Construction of the Distance Graph Used for the Classification
课程网址: http://videolectures.net/gbr07_kalinowski_afc/  
主讲教师: Miroslaw Kalinowski
开课单位: 纽约城市大学
开课时间: 2007-07-12
课程语种: 英语
中文简介:
研究表明,与大分子三维电子显微镜(3d-em)中发现的不均匀投影图像相似,通过找到一个适当构造的加权图的近似最大k-切点,可以成功地解决不均匀投影图像分类的难题。尽管图的大尺寸(数千个节点)和理论计算的复杂性,甚至找到一个近似的最大k-截,一个算法已经提出,找到一个好的(从分类的角度)近似解在几分钟内(运行在一个标准的PC上)。然而,构建完整的加权图(代表投影图像分类问题的一个实例)的任务在计算上是昂贵的。由于边的数量很大,对于包含数千个节点的图,边权重的计算可能需要几十个小时。我们提出了一种利用早期终止技术的方法,以显著降低构造此类图的计算成本。我们比较了类似于3D-EM中投影集的合成数据集,该方法的性能与蛮力法和基于最近邻搜索的方法的性能。
课程简介: It has been demonstrated that the diffcult problem of classifying heterogeneous projection images, similar to those found in 3D electron microscopy (3D-EM) of macromolecules, can be successfully solved by finding an approximate Max k-Cut of an appropriately constructed weighted graph. Despite of the large size (thousands of nodes) of the graph and the theoretical computational complexity of finding even an approximate Max k-Cut, an algorithm has been proposed that finds a good (from the classification perspective) approximate solution within several minutes (running on a standard PC). However, the task of constructing the complete weighted graph (that represents an instance of the projection image classification problems) is computationally expensive. Due to the large number of edges, the computation of edge weights can take tens of hours for graphs containing several thousand nodes. We propose a method, which utilizes an early termination technique, to significantly reduce the computational cost of constructing such graphs. We compare, on synthetic data sets that resemble projection sets encountered in 3D-EM, the performance of our method with that of a brute-force approach and a method based on nearest neighbor search.
关 键 词: 投影图像; 边缘权重; 邻搜索方法
课程来源: 视频讲座网
最后编审: 2019-11-30:lxf
阅读次数: 42