![](/images/graphics-bg.png)
Empirical Validation of Objective Functions in Feature Selection Based on Acceleration Motion Segmentation Data
Joint Authors
Lim, Jong Gwan
Kim, Mi-hye
Lee, Sahngwoon
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-11
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Lim, Jong Gwan& Kim, Mi-hye& Lee, Sahngwoon. 2015. Empirical Validation of Objective Functions in Feature Selection Based on Acceleration Motion Segmentation Data. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1073395
Modern Language Association (MLA)
Lim, Jong Gwan…[et al.]. Empirical Validation of Objective Functions in Feature Selection Based on Acceleration Motion Segmentation Data. Mathematical Problems in Engineering No. 2015 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1073395
American Medical Association (AMA)
Lim, Jong Gwan& Kim, Mi-hye& Lee, Sahngwoon. Empirical Validation of Objective Functions in Feature Selection Based on Acceleration Motion Segmentation Data. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-12.
https://search.emarefa.net/detail/BIM-1073395
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1073395