Combining Supervised and Unsupervised Learning to Discover Emotional Classes

Miguel Arevalillo-Herráez, Aladdin Ayesh, Olga C. Santos, Pablo Arnau-González

    Research output: Chapter in Book/Report/Conference proceedingChapter


    Most previous work in emotion recognition has fixed the available classes in advance, and attempted to classify samples into one of these classes using a supervised learning approach. In this paper, we present preliminary work on combining supervised and unsupervised learning to discover potential latent classes which were not initially considered. To illustrate the potential of this hybrid approach, we have used a Self-Organizing Map (SOM) to organize a large number of Electroencephalogram (EEG) signals from subjects watching videos, according to their internal structure. Results suggest that a more useful labelling scheme could be produced by analysing the resulting topology in relation to user reported valence levels (i.e., pleasantness) for each signal, refining the original set of target classes.
    Original languageEnglish
    Title of host publicationUMAP '17: Proceedings of the 25th Conference on User Modeling, Adaptation and PersonalizationJuly 2017
    Publication statusPublished - 9 Jul 2017


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