Computer Science > Machine Learning
arXiv:2304.00709 (cs)
[Submitted on 3 Apr 2023 (v1), last revised 9 Jul 2024 (this version, v3)]
Title:MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstruction
View a PDF of the paper titled MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstruction, by Xu Tan and 4 other authors
View PDFHTML (experimental)Abstract:AutoEncoders (AEs) are commonly used for machine learning tasks due to their intrinsic learning ability. This unique characteristic can be capitalized for Outlier Detection (OD). However conventional AE-based methods face the issue of overconfident decisions and unexpected reconstruction results of outliers, limiting their performance in OD. To mitigate these issues, the Mean Squared Error (MSE) and Negative Logarithmic Likelihood (NLL) were firstly analyzed, and the importance of incorporating aleatoric uncertainty to AE-based OD was elucidated. Then the Weighted Negative Logarithmic Likelihood (WNLL) was proposed to adjust for the effect of uncertainty for different OD scenarios. Moreover, the Mean-Shift Scoring (MSS) method was proposed to utilize the local relationship of data to reduce the issue of false inliers caused by AE. Experiments on 32 real-world OD datasets proved the effectiveness of the proposed methods. The combination of WNLL and MSS achieved 41% relative performance improvement compared to the best baseline. In addition, MSS improved the detection performance of multiple AE-based outlier detectors by an average of 20%. The proposed methods have the potential to advance AE's development in OD.
| Comments: | 31 pages, 11 figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2304.00709 [cs.LG] |
| (orarXiv:2304.00709v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2304.00709 arXiv-issued DOI via DataCite |
Submission history
From: Xu Tan [view email][v1] Mon, 3 Apr 2023 04:01:29 UTC (4,448 KB)
[v2] Tue, 9 Apr 2024 11:24:34 UTC (41,087 KB)
[v3] Tue, 9 Jul 2024 06:08:17 UTC (11,190 KB)
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View a PDF of the paper titled MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected Reconstruction, by Xu Tan and 4 other authors
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