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- W2904265151 abstract "Identifying different types of multi-view data outliers with abnormal behaviors is an interesting yet challenging unsupervised learning task, 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. 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. In this chapter, we propose a novel multi-view outlier detection method with a consensus regularization on the latent representations." @default.
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- W2904265151 date "2018-12-07" @default.
- W2904265151 modified "2023-09-23" @default.
- W2904265151 title "Multi-view Outlier Detection" @default.
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- W2904265151 doi "https://doi.org/10.1007/978-3-030-00734-8_4" @default.
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