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|a 10.1109/TIP.2017.2754942
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|a eng
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|a Handong Zhao
|e verfasserin
|4 aut
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|a Consensus Regularized Multi-View Outlier Detection
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|c 2018
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|a Date Completed 11.12.2018
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|a Date Revised 11.12.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Identifying different types of data outliers with abnormal behaviors in multi-view data setting is challenging due to the complicated data distributions across different views. Conventional approaches achieve this by learning a new latent feature representation with the pairwise constraint on different view data. In this paper, we argue that the existing methods are expensive in generalizing their models from two-view data to three-view (or more) data, in terms of the number of introduced variables and detection performance. To address this, we propose a novel multi-view outlier detection method with consensus regularization on the latent representations. Specifically, we explicitly characterize each kind of outliers by the intrinsic cluster assignment labels and sample-specific errors. Moreover, we make a thorough discussion about the proposed consensus-regularization and the pairwise-regularization. Correspondingly, an optimization solution based on augmented Lagrangian multiplier method is proposed and derived in details. In the experiments, we evaluate our method on five well-known machine learning data sets with different outlier settings. Further, to show its effectiveness in real-world computer vision scenario, we tailor our proposed model to saliency detection and face reconstruction applications. The extensive results of both standard multi-view outlier detection task and the extended computer vision tasks demonstrate the effectiveness of our proposed method
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Hongfu Liu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhengming Ding
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yun Fu
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 27(2018), 1 vom: 01. Jan., Seite 236-248
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|g year:2018
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|g pages:236-248
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|u http://dx.doi.org/10.1109/TIP.2017.2754942
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