A comparison of PCA, KPCA and LDA for feature extraction to recognize affect in gait kinematics

@article{Karg2009ACO,
  title={A comparison of PCA, KPCA and LDA for feature extraction to recognize affect in gait kinematics},
  author={Michelle Karg and Robert Jenke and Wolfgang Seiberl and K. Kuuhnlenz and A. Schwirtz and Martin Buss},
  journal={2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops},
  year={2009},
  pages={1-6}
}
This study investigates recognition of affect in human walking as daily motion, in order to provide a means for affect recognition at distance. For this purpose, a data base of affective gait patterns from non-professional actors has been recorded with optical motion tracking. Principal component analysis (PCA), kernel PCA (KPCA) and linear discriminant analysis (LDA) are applied to kinematic parameters and compared for feature extraction. LDA in combination with naive Bayes leads to an… CONTINUE READING
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