Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection
Chen Z, Yeo CK, Lee BS, Lau CT, Jin Y (2018)
Neurocomputing 309: 192-200.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Chen, Zhaomin;
Yeo, Chai Kiat;
Lee, Bu Sung;
Lau, Chiew Tong;
Jin, YaochuUniBi ![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
![](https://pub.uni-bielefeld.de/images/icon_orcid.png)
Abstract / Bemerkung
Image outlier detection has been an important research issue for many computer vision tasks. However, most existing outlier detection methods fail in the high-dimensional image datasets. In order to address this problem, we propose a novel image outlier detection method by combining autoencoder with Adaboost (ADAE). By ensembling many weak autoencoders, our method can better capture the statistical correlations among the features of normal data than the single autoencoder. Therefore, the proposed ADAE is able to determine the outliers efficiently. In order to reduce the many parameters in ADAE, we introduce the Sparse Group Lasso (SGL) constraint into the learning objective of ADAE. We combine Adagrad with Proximal Gradient Descent to optimize this additional learning objective. We also propose the multi-objective evolutionary algorithm to determine the best penalty factors of SGL. By evaluating on several famous image datasets, the detection results testify to the outstanding outlier detection performance of ADAE. The evaluation results also show SGL can make the detection model more compact while maintaining the similar detection performance.
Erscheinungsjahr
2018
Zeitschriftentitel
Neurocomputing
Band
309
Seite(n)
192-200
ISSN
0925-2312
Page URI
https://pub.uni-bielefeld.de/record/2978457
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Chen Z, Yeo CK, Lee BS, Lau CT, Jin Y. Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection. Neurocomputing. 2018;309:192-200.
Chen, Z., Yeo, C. K., Lee, B. S., Lau, C. T., & Jin, Y. (2018). Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection. Neurocomputing, 309, 192-200. https://doi.org/10.1016/j.neucom.2018.05.012
Chen, Zhaomin, Yeo, Chai Kiat, Lee, Bu Sung, Lau, Chiew Tong, and Jin, Yaochu. 2018. “Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection”. Neurocomputing 309: 192-200.
Chen, Z., Yeo, C. K., Lee, B. S., Lau, C. T., and Jin, Y. (2018). Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection. Neurocomputing 309, 192-200.
Chen, Z., et al., 2018. Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection. Neurocomputing, 309, p 192-200.
Z. Chen, et al., “Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection”, Neurocomputing, vol. 309, 2018, pp. 192-200.
Chen, Z., Yeo, C.K., Lee, B.S., Lau, C.T., Jin, Y.: Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection. Neurocomputing. 309, 192-200 (2018).
Chen, Zhaomin, Yeo, Chai Kiat, Lee, Bu Sung, Lau, Chiew Tong, and Jin, Yaochu. “Evolutionary multi-objective optimization based ensemble autoencoders for image outlier detection”. Neurocomputing 309 (2018): 192-200.
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