基于捕获数据的降维分析与比较

Zhijun Zheng
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引用次数: 4

摘要

由于运动在捕获原始数据时的高维特征,高维原始数据将被投影到低维子空间中。身体运动的内部结构将通过这个低维空间被揭示出来。消除高维特征的相关冗余信息成为三维运动捕捉数据的关键技术。本文采用基于几种机器学习方法的关键帧和降维方法来处理运动捕捉数据。经过一系列的实验结果,非线性子空间具有更好的性能和更广泛的可用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis and Comparison of Dimensional Reduction Based on Capture Data
owing to the high dimension characteristic of motion in catching original data, the high dimensional original data will be projected into low dimensional sub space. The internal structure of body motion will be revealed through this low dimensional space. The elimination of the related redundant information of high dimensional characteristics becomes key technology for 3D motion capture data. This paper applies key frame and dimension reduction method based on several machine learning methods to handle motion capture data. After a series of experimental results, non-linear sub space is of better performance and wider availability.
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