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...
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Format: | Article |
Language: | English |
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MDPI AG
2019-10-01
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Series: | Symmetry |
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Online Access: | https://www.mdpi.com/2073-8994/11/10/1264 |