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Empirical Validation of Objective Functions in Feature Selection Based on Acceleration Motion Segmentation Data

机译：基于加速运动分割数据的特征选择目标函数的经验验证

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摘要

Recent change in evaluation criteria from accuracy alone to trade-off with time delay has inspired multivariate energy-based approaches in motion segmentation using acceleration. The essence of multivariate approaches lies in the construction of highly dimensional energy and requires feature subset selection in machine learning. Due to fast process, filter methods are preferred; however, their poorer estimate is of the main concerns. This paper aims at empirical validation of three objective functions for filter approaches, Fisher discriminant ratio, multiple correlation (MC), and mutual information (MI), through two subsequent experiments. With respect to 63 possible subsets out of 6 variables for acceleration motion segmentation, three functions in addition to a theoretical measure are compared with two wrappers, k-nearest neighbor and Bayes classifiers in general statistics and strongly relevant variable identification by social network analysis. Then four kinds of new proposedmultivariate energy are compared with a conventional univariate approach in terms of accuracy and time delay. Finally it appears that MC and MI are acceptable enough to match the estimate of two wrappers, and multivariate approaches are justified with our analytic procedures.
机译：评估标准的最新变化，从单纯的准确性到权衡时间的延迟，启发了使用加速度的运动分割中基于能量的多元方法。多元方法的本质在于高维能量的构建，并且需要在机器学习中选择特征子集。由于处理速度快，因此首选过滤方法。然而，他们较差的估计是主要问题。本文旨在通过两个后续实验，对三个目标函数进行滤波方法，费舍尔判别率，多重相关（MC）和互信息（MI）进行经验验证。对于用于加速运动分割的6个变量中的63个可能的子集，除了理论上的度量外，还将三个函数与一般统计中的两个包装器，k近邻和贝叶斯分类器进行比较，并通过社交网络分析对变量进行高度相关的识别。然后在准确性和时间延迟方面，将四种新提出的多元能量与常规单变量方法进行了比较。最终，MC和MI似乎足以接受两个包装器的估计值，并且我们的分析程序证明了多元方法的合理性。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第20期|280140.1-280140.12|共12页
  • 作者

    Lim Jong Gwan; Kim Mi-hye; Lee Sahngwoon;

  • 作者单位

    Korea Adv Inst Sci & Technol, Daejeon 305338, South Korea;

    Chungbuk Natl Univ, Cheongju 362763, Chungbuk, South Korea;

    Systran Int, Seoul 135855, South Korea;

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