Multi-View Temporal Ensemble for Classification of Non-Stationary Signals
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8662555/ |
| _version_ | 1819276313123356672 |
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| author | B. H. D. Koh Wai Lok Woo |
| author_facet | B. H. D. Koh Wai Lok Woo |
| author_sort | B. H. D. Koh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T23:38:14Z |
| format | Article |
| id | doaj.art-eefd325ead52414d9844a030c543cd09 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:38:14Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eefd325ead52414d9844a030c543cd092022-12-21T17:25:49ZengIEEEIEEE Access2169-35362019-01-017324823249110.1109/ACCESS.2019.29035718662555Multi-View Temporal Ensemble for Classification of Non-Stationary SignalsB. H. D. Koh0https://orcid.org/0000-0001-8937-5359Wai Lok Woo1School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, U.K.School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, U.K.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.https://ieeexplore.ieee.org/document/8662555/Deep learningdata fusiontime series classification |
| spellingShingle | B. H. D. Koh Wai Lok Woo Multi-View Temporal Ensemble for Classification of Non-Stationary Signals IEEE Access Deep learning data fusion time series classification |
| title | Multi-View Temporal Ensemble for Classification of Non-Stationary Signals |
| title_full | Multi-View Temporal Ensemble for Classification of Non-Stationary Signals |
| title_fullStr | Multi-View Temporal Ensemble for Classification of Non-Stationary Signals |
| title_full_unstemmed | Multi-View Temporal Ensemble for Classification of Non-Stationary Signals |
| title_short | Multi-View Temporal Ensemble for Classification of Non-Stationary Signals |
| title_sort | multi view temporal ensemble for classification of non stationary signals |
| topic | Deep learning data fusion time series classification |
| url | https://ieeexplore.ieee.org/document/8662555/ |
| work_keys_str_mv | AT bhdkoh multiviewtemporalensembleforclassificationofnonstationarysignals AT wailokwoo multiviewtemporalensembleforclassificationofnonstationarysignals |