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Support vector regression based autoassociative models for time series classification

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2 Author(s)
Chandrakala, S. ; Dept. of Comput. Sci. & Eng., Indian Inst. of Technol. Madras, Chennai, India ; Sekhar, C.C.

There are two paradigms for modeling varying length time series data, namely, modeling the sequence of feature vectors and modeling the sets of vectors. In this paper, we propose a regression based autoassociative model for modeling sets of vectors for time series data. We also propose a hybrid framework where a regression based autoassociative model is used for representing varying length time series data and then a discriminative model is used for classification. The proposed approach applied to speech emotion recognition task gives a better performance than the conventional methods.

Published in:

Communications (NCC), 2010 National Conference on

Date of Conference:

29-31 Jan. 2010

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