Learning probabilistic latent structure for outlier detection from multi-view data
Mining anomalous objects from multi-view data is a challenging issue as data collected from
diverse sources have more complicated distributions and exhibit inconsistently
heterogeneous properties. Existing multi-view outlier detection approaches mainly focus on
transduction, which becomes very costly when new data points are introduced by an input
stream. Besides, the existing detection methods use either the pairwise multiplication of
cross-view data vectors to quantify outlier scores or the predicted joint probability to measure …
diverse sources have more complicated distributions and exhibit inconsistently
heterogeneous properties. Existing multi-view outlier detection approaches mainly focus on
transduction, which becomes very costly when new data points are introduced by an input
stream. Besides, the existing detection methods use either the pairwise multiplication of
cross-view data vectors to quantify outlier scores or the predicted joint probability to measure …
Learning Probabilistic Latent Structure for Outlier Detection from Multi-view Data
RU Kiran, JCW Lin, J Zhang, Z Wang, J Xiao, C Lu… - (No Title), 2021 - cir.nii.ac.jp
Mining anomalous objects from multi-view data is a challenging issue as data collected from
diverse sources have more complicated distributions and exhibit inconsistently
heterogeneous properties. Existing multi-view outlier detection approaches mainly focus on
transduction, which becomes very costly when new data points are introduced by an input
stream. Besides, the existing detection methods use either the pairwise multiplication of
cross-view data vectors to quantify outlier scores or the predicted joint probability to measure …
diverse sources have more complicated distributions and exhibit inconsistently
heterogeneous properties. Existing multi-view outlier detection approaches mainly focus on
transduction, which becomes very costly when new data points are introduced by an input
stream. Besides, the existing detection methods use either the pairwise multiplication of
cross-view data vectors to quantify outlier scores or the predicted joint probability to measure …
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