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Multi-View Temporal Ensemble for Classification of Non-Stationary Signals

Lookup NU author(s): Bee Koh, Dr Wai Lok Woo

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Abstract

© 2013 IEEE. In the classification of non-stationary time series data such as sounds, it is often tedious and expensive to get a training set that is representative of the target concept. To alleviate this problem, the proposed method treats the outputs of a number of deep learning sub-models as the views of the same target concept that can be linearly combined according to their complementarity. It is proposed that the view's complementarity be the contribution of the view to the global view, chosen in this paper to be the Laplacian eigenmap of the combined data. Complementarity is computed by alternate optimization, a process that involves the cost function of the Laplacian eigenmap and the weights of the linear combination. By blending the views in this way, a more complete view of the underlying phenomenon can be made available to the final classifier. Better generalization is obtained, as the consensus between the views reduces the variance while the increase in the discriminatory information reduces the bias. The data experiment with artificial views of environment sounds formed by deep learning structures of different configurations shows that the proposed method can improve the classification performance.

Publication metadata

Author(s): Koh BHD, Woo WL

Publication type: Article

Publication status: Published

Journal: IEEE Access

Year: 2019

Volume: 7

Pages: 32482-32491

Online publication date: 07/03/2019

Acceptance date: 03/03/2019

ISSN (electronic): 2169-3536

Publisher: IEEE

URL: https://doi.org/10.1109/ACCESS.2019.2903571

DOI: 10.1109/ACCESS.2019.2903571

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