Learning Sparse Latent Graph Representations for Anomaly Detection in Multivariate Time Series-Reference-Cited by-同舟云学术

Learning Sparse Latent Graph Representations for Anomaly Detection in Multivariate Time Series

Published:2022-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Han Siho¹,Woo Simon S.²

Affiliation:

1. RTM, Seoul, South Korea

2. Sungkyunkwan University, Suwon, South Korea

Funder

IITP by Korean MSIT (Artificial Intelligence Innovation Hub)

Basic Science Research Program through NRF by Korean MSIT

IITP by Korean MSIT (Graduate School of Convergence Security at Sungkyunkwan University)

IITP by Korean MSIT (Self-directed Multi-Modal Intelligence for solving unknown, open domain problems)

IITP by Korean MSIT (AI Platform to Fully Adapt and Reflect Privacy-Policy Changes)

IITP by Korean MSIT (AI Graduate School Support Program at Sungkyunkwan University)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539117

Reference38 articles.

1. Charu C Aggarwal . 2015. Outlier analysis . In Data mining . Springer , 237--263. Charu C Aggarwal. 2015. Outlier analysis. In Data mining. Springer, 237--263.

2. WADI

3. Variational autoencoder based anomaly detection using reconstruction probability;An Jinwon;Special Lecture on IE,2015

4. Fast Outlier Detection in High Dimensional Spaces

5. USAD

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