Combining Supervised and Unsupervised Learning to Discover Emotional Classes

Date

2017-07-09

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

ACM

Type

Conference

Peer reviewed

Yes

Abstract

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.

Description

Keywords

class discovery, user modelling, affective computing, cluster analysis, EEG

Citation

Arevalillo-Herráez, M., Ayesh, A., Santos, OC, and Arnau-González, P. (2017) Combining Supervised and Unsupervised Learning to Discover Emotional Classes. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP '17). ACM, New York, NY, USA, pp.355-356.

Rights

Research Institute

Cyber Technology Institute (CTI)
Institute of Artificial Intelligence (IAI)