Authors:
Husam Al-Behadili
1
;
Arne Grumpe
2
and
Christian Wöhler
2
Affiliations:
1
University of Mustansiriyah and TU Dortmund University, Iraq
;
2
TU Dortmund University, Germany
Keyword(s):
Data Stream, Neural Network, Extreme learning Machine (ELM), Novelty Detection, Incremental Learning, Semi-supervised Learning, Extreme Value Theory (EVT), Confidence Band.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Human and Computer Interaction
;
Human-Computer Interaction
Abstract:
The problems of infinitely long data streams and its concept drift as well as non-linearly separable classes
and the possible emergence of “novel classes” are topics of high relevance for gesture data streaming based
automatic recognition systems. To address these problems we apply a semi-supervised learning technique
using a neural network in combination with an incremental update rule. Neural networks have been shown
to handle non-linearly separable data and the incremental update ensures that the parameters of the classifier
follow the “concept-drift” without the necessity of an increased training set. Since a semi-supervised learning
technique is sensitive to false labels, we apply an outlier detection method based on extreme value theory and
confidence band intervals. The proposed algorithm uses the extreme learning machine, which is easily updated
and works with multi-classes. A comparison with an auto-encoder neural network shows that the proposed
algorithm has superior proper
ties. Especially, the processing time is greatly reduced.
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