Consensus Regularized Multi-View Outlier Detection
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 differ...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 27(2018), 1 vom: 01. Jan., Seite 236-248 |
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| Weitere Verfasser: | , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2018
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | 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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| Beschreibung: | Date Completed 11.12.2018 Date Revised 11.12.2018 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2017.2754942 |