Authors:
Hany Ferdinando
1
;
Tapio Seppänen
2
and
Esko Alasaarela
2
Affiliations:
1
University of Oulu and Petra Christian University, Finland
;
2
University of Oulu, Finland
Keyword(s):
Emotion Recognition, kNN, Dimensionality Reduction, LDA, NCA, MCML.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Cardiovascular Imaging and Cardiography
;
Cardiovascular Technologies
;
Health Engineering and Technology Applications
;
Learning of Action Patterns
;
Pattern Recognition
;
Signal Processing
;
Software Engineering
Abstract:
Dimensionality reduction (DR) is an important issue in classification and pattern recognition process. Using
features with lower dimensionality helps the machine learning algorithms work more efficient. Besides, it
also can improve the performance of the system. This paper explores supervised dimensionality reduction,
LDA (Linear Discriminant Analysis), NCA (Neighbourhood Components Analysis), and MCML (Maximally
Collapsing Metric Learning), in emotion recognition based on ECG signals from the Mahnob-HCI database.
It is a 3-class problem of valence and arousal. Features for kNN (k-nearest neighbour) are based on statistical
distribution of dominant frequencies after applying a bivariate empirical mode decomposition. The results
were validated using 10-fold cross and LOSO (leave-one-subject-out) validations. Among LDA, NCA, and
MCML, the NCA outperformed the other methods. The experiments showed that the accuracy for valence
was improved from 55.8% to 64.1%, and for arousal from 59.7%
to 66.1% using 10-fold cross validation after
transforming the features with projection matrices from NCA. For LOSO validation, there is no significant
improvement for valence while the improvement for arousal is significant, i.e. from 58.7% to 69.6%.
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