Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)

Chong Hyun Lee; Kibae Lee

doi:10.1117/12.2661178

14 June 2023 Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)

Chong Hyun Lee, Kibae Lee

Proceedings Volume 12531, Anomaly Detection and Imaging with X-Rays (ADIX) VIII; 125310M (2023) https://doi.org/10.1117/12.2661178
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

The unlabeled data are generally assumed to be normal data in anomaly detection (AD) based on semi-supervised learning. This assumption, however, can degrade detection performance when distributions of unlabeled and labeled data are different. To solve the problem, we propose a semi-supervised AD algorithm using KL divergence (SAD-KL) estimating the KL divergence of PDFs of the local outlier factors (LOFs) of the labeled normal and the unlabeled data. We show that the PDFs of the LOFs follow Burr distribution and the SAD-KL shows superior detection probability over the existing algorithms even though it takes less learning time.

Citation Download Citation

Chong Hyun Lee and Kibae Lee "Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)", Proc. SPIE 12531, Anomaly Detection and Imaging with X-Rays (ADIX) VIII, 125310M (14 June 2023); https://doi.org/10.1117/12.2661178

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available