![]() ![]() We find that deep neural networks fusing the probabilistic method of sample multi-classification can capture these desired low-dimensional features moreover, these captured low-dimensional features present more obvious layered characteristics. Finally, experimental results on synthetic and real-world data sets show that the proposed method not only outperforms the state-of-the-art methods in the precision of mined anomalies, but also this hybrid method consisting of deep neural networks and traditional detection methods has outstanding capabilities of mining high-dimensional anomalies. In the low-dimensional features extracted by the deep neural network, the anomalous detector separates anomaly features from normal features. To promote the ability of the deep neural network to capture these features, the probability approach of sample binary-classification is fused into the loss function, thereby forming the probability deep neural network Then, the hypersphere is used as an anomalous detector. In the proposed method, the deep neural network is used as a feature extractor to capture those layered low-dimensional features from the data lying in a high-dimensional space. To address this, here proposed a deep hypersphere method fused with probabilistic approach for anomaly mining. Consequently, it is a challenge for anomaly mining in a high-dimensional space. This also creates trouble for anomaly mining. Moreover, a high-dimensional space may exist many subspaces, obviously, anomalies can exist in any subspaces. Data distribution presents sparsity in a high-dimensional space, thus difficulty affording sufficient information to distinguish anomalies from normal instances.
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