Neural Network based Novelty Detection for Incremental Semi-supervised Learning in Multi-class Gesture Recognition

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 properties. Especially, the processing time is greatly reduced.