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

Koh, Bee Hock David and Woo, Wai Lok (2019) Multi-view Temporal Ensemble for Classification of Non-Stationary Signals. IEEE Access, 7. pp. 32482-32491. ISSN 2169-3536

Koh, Woo - Multi-view Temporal Ensemble for Classification of Non-Stationary Signals AAM.pdf - Accepted Version

Download (832kB) | Preview
08662555.pdf - Published Version

Download (3MB) | Preview
Official URL:


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 work 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. 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.

Item Type: Article
Uncontrolled Keywords: deep learning, data fusion, time series classification
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 06 Mar 2019 14:21
Last Modified: 01 Aug 2021 12:04

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics