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Evolutionary Multi-Objective Optimization based Ensemble Autoencoders for Image Outlier Detection

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. pp. 192-200.

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Abstract

Image outlier detection has been an important research issue for many computer vision tasks. However, most existing outlier detection methods fail in the highdimensional 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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Chen, Zhaominz.chen@surrey.ac.uk
Yeo, Chai Kiat
Lee, Bu Sung
Lau, Chiew Tong
Jin, YaochuYaochu.Jin@surrey.ac.uk
Date : 11 May 2018
DOI : 10.1016/j.neucom.2018.05.012
Copyright Disclaimer : © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords : Image Outlier Detection; Autoencoder; Adaboost; Sparse Group Lasso (SGL); Multi-objective Evolutionary Algorithm; Adagrad Proximal Gradient Descent (Ada-PGD)
Related URLs :
Depositing User : Clive Harris
Date Deposited : 08 May 2018 15:21
Last Modified : 12 May 2019 02:08
URI: http://epubs.surrey.ac.uk/id/eprint/846373

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