Classification of temporal data based on self-organizing incremental neural network

Authors:
Shogo Okada;Osamu Hasegawa
Affiliations:
Department of Computer Intelligence and Systems Science, Tokyo Institute of Technology;Imaging Science and Engineering Laboratory, Tokyo Institute of Technology
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

This paper presents an approach (SOINN-DTW) for recognition of temporal data that is based on Self-Organizing Incremental Neural Network (SOINN) and Dynamic Time Warping. Using SOINN's function that eliminates noise in the input data and represents topological structure of input data, SOINN-DTW method approximates output distribution of each state and is able to construct robust model for temporal data. SOINN-DTW method is the novel method that enhanced Stochastic Dynamic Time Warping Method (Nakagawa,1986). To confirm the effectiveness of SOINN-DTW method, we present an extensive set of experiments that show how our method outperforms HMM and Stochastic Dynamic TimeWarping Method in classifying phone data and gesture data.