Title
Using unlabeled data to improve classification of emotional states in human computer interaction
Abstract
The individual nature of physiological measurements of human affective states makes it very difficult to transfer statistical classifiers from one subject to another. In this work, we propose an approach to incorporate unlabeled data into a supervised classifier training in order to conduct an emotion classification. The key idea of the method is to conduct a density estimation of all available data (labeled and unlabeled) to create a new encoding of the problem. Based on this a supervised classifier is constructed. Further, numerical evaluations on the EmoRec II corpus are given, examining to what extent additional data can improve classification and which parameters of the density estimation are optimal.
Year
DOI
Venue
2014
10.1007/s12193-013-0133-0
Journal on Multimodal User Interfaces
Keywords
Field
DocType
Partially supervised learning,Clustering,Affective computing
Density estimation,Semi-supervised learning,Computer science,Emotion classification,Human–computer interaction,Artificial intelligence,Cluster analysis,Classifier (linguistics),Pattern recognition,Affective computing,Linear classifier,Machine learning,Encoding (memory)
Journal
Volume
Issue
ISSN
8
1
1783-7677
Citations 
PageRank 
References 
16
0.65
25
Authors
6
Name
Order
Citations
PageRank
Martin Schels127715.88
Markus Kächele222214.76
Michael Glodek329516.76
David Hrabal4736.01
Steffen Walter512713.34
Friedhelm Schwenker6116096.59