Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features

Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features

This paper proposes a method for improving human motion classification by applying bagging and symmetry to Principal Component Analysis (PCA)-based features. In contrast to well-known bagging algorithms such as random forest, the proposed method recalculates the motion features for each “w...

Full description

Bibliographic Details
Main Author: Tomasz Hachaj
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Symmetry
Subjects:
classification
bagging
motion capture
principal component analysis
support vector machine
nearest-neighbor classifier
motion symmetry
Online Access:https://www.mdpi.com/2073-8994/11/10/1264

Internet

https://www.mdpi.com/2073-8994/11/10/1264

Similar Items