2026-04-10T17:49:51Z https://ipsj.ixsq.nii.ac.jp/oai

oai:ipsj.ixsq.nii.ac.jp:00195507 2025-01-19T23:04:20Z 581:9633:9637

Low-cost Unsupervised Outlier Detection by Autoencoders with Robust Estimation Low-cost Unsupervised Outlier Detection by Autoencoders with Robust Estimation Yoshinao, Ishii Masaki, Takanashi Yoshinao, Ishii Masaki, Takanashi [一般論文（テクニカルノート）] outlier detection, unsupervised learning, autoencoder, robust estimation Recently, an unsupervised outlier detection method based on the reconstruction errors of an autoencoder (AE), which achieves high detection accuracy, was proposed. This method, however, requires a high calculation cost because of its ensemble scheme. Therefore, in this paper, we propose a novel AE-based unsupervised method that can achieve high detection performance at a low calculation cost. Our method introduces the concept of robust estimation to appropriately restrict reconstruction capability and ensure robustness. Experimental results on several public benchmark datasets show that our method outperforms well-known outlier detection methods and at a low calculation cost. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.27(2019) (online) DOI　http://dx.doi.org/10.2197/ipsjjip.27.335 ------------------------------ Recently, an unsupervised outlier detection method based on the reconstruction errors of an autoencoder (AE), which achieves high detection accuracy, was proposed. This method, however, requires a high calculation cost because of its ensemble scheme. Therefore, in this paper, we propose a novel AE-based unsupervised method that can achieve high detection performance at a low calculation cost. Our method introduces the concept of robust estimation to appropriately restrict reconstruction capability and ensure robustness. Experimental results on several public benchmark datasets show that our method outperforms well-known outlier detection methods and at a low calculation cost. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.27(2019) (online) DOI　http://dx.doi.org/10.2197/ipsjjip.27.335 ------------------------------ 2019-04-15 eng journal article https://ipsj.ixsq.nii.ac.jp/records/195507 1882-7764 AN00116647 情報処理学会論文誌 60 4 https://ipsj.ixsq.nii.ac.jp/record/195507/files/IPSJ-JNL6004011.pdf application/pdf 524.3 kB 2021-04-15