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Mathematical Problems in Engineering
Volume 2015, Article ID 280140, 12 pages
Research Article

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

1KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 305-338, Republic of Korea
2Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 362-763, Republic of Korea
3Systran International, 163 Yangjaecheon-ro, Gangnam-gu, Seoul 135-855, Republic of Korea

Received 5 March 2015; Accepted 14 April 2015

Academic Editor: Sanghyuk Lee

Copyright © 2015 Jong Gwan Lim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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 proposed multivariate 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.