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• W4313398213 endingPage "110209" @default.
• W4313398213 startingPage "110209" @default.
• W4313398213 abstract "Deep-learning-based anomaly detection methods achieve excellent performance with the help of powerful feature extraction capabilities. However, the existing methods ignore the fine-grained dimension contributions of low-dimensional embeddings to anomaly analysis from different perspectives. Therefore, we propose an unsupervised Dimension-Contribution-aware Embeddings Transformation method for Anomaly Detection (DCETAD), focusing on the different contributions of the crucial dimensional information of low-dimensional embeddings to anomaly detection from different perspectives. Specifically, we design the Dimension-Contribution-aware Sparse AutoEncoder (DC-SAE) to learn more discriminative embeddings on the basis of suppressing the sub-optimal and useless dimensions and highlight the dimension contributions of low-dimensional embeddings; then, perform the affine transformation to analyze the dimension contributions of low-dimensional embeddings in different transformation subspaces; finally, construct these transformation subspaces as multiple hyperspheres to comprehensively evaluate the anomaly score. Experimental results demonstrate that DCETAD not only achieves excellent detection performance but also outperforms recent state-of-the-art models." @default.
• W4313398213 created "2023-01-06" @default.
• W4313398213 creator A5000337879 @default.
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• W4313398213 date "2023-02-01" @default.
• W4313398213 modified "2023-09-27" @default.
• W4313398213 title "Unsupervised dimension-contribution-aware embeddings transformation for anomaly detection" @default.
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• W4313398213 doi "https://doi.org/10.1016/j.knosys.2022.110209" @default.
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