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High Dimensional Data Reduction using approximate Convex Hulls

High Dimensional Data Reduction using approximate Convex Hulls
使用近似凸包进行高维数据缩减
批准号：
486596-2015
负责人：
Oberman, Adam
金额：
$1.82万
依托单位：
McGill University
依托单位国家：
加拿大
项目类别：
Engage Grants Program
财政年份：
2015
资助国家：
加拿大
项目状态：
已结题
起止时间：
2015-01-01 至 2016-12-31
来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=585692
关键词：
DimensionalDataReductionusingapproximate

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英文摘要
This project is a joint effort between applied mathematics and aerospace engineering. It aims to use mathematics to reduce the number of data points required for structural stability testing of materials. Materials must be tested (simulated) under a wide range of conditions, each of which is represented by a high dimensional data point. However each test is time consuming and computationally intensive. We can reduce the number of tests by using only extreme values of the data points. In high dimensions, the convex hull is used to find these extreme values. However existing algorithms for the convex hull are limited to small dimensions. This research proposes to find a more efficient way to reduce the data using a modified convex hull which can be computed effectively in higher dimensions.
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