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 describes an emotion recognition system based on human biosignal patterns. The emotion recognition system can recognize a user's emotional status by ...
This paper explores supervised dimensionality reduction, LDA (Linear Discriminant Analysis), NCA (Neighbourhood Components Analysis), and MCML (Maximally ...
Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction. Hany Ferdinando, Tapio Seppänen, Esko Alasaarela. 2017. Abstract.
Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction ; Publication format. Article. Parent publication type. Conference ...
Paper. Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction. Topics: Learning of Action Patterns; Signal Processing. In ...
This work presents a novel emotion recognition algorithm based on wavelet scattering feature extraction and supervised machine learning and shows its ...
Firstly, ECG signals capture the heart activity, and ANS stimulation towards each emotion causes rhythmic changes in the heart [25]. Secondly, an ECG can be ...
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DOI. 10.1109/ISGT-LA.2019.8895285. 2019. Enhancing Emotion Recognition from ECG Signals using Supervised Dimensionality Reduction. Peer-reviewed.
This paper proposes a new strategy for EEG emotion recognition utilizing Riemannian geometry with the aim of achieving better classification performance.