.png){kind=icon}
.png){kind=icon}
Return
Missing-Kernel-Based OCSVM for Anomaly Detection on Missing Data
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2026.3695800.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Anomaly detection is challenging with the presence of missing data. The common two-stage strategy first imputes missing values and then performs anomaly detection, making its performance highly dependent on imputation quality. When the data distribution is complex, the imputation quality may be poor, which degrades the performance of anomaly detectors. To address this issue, we propose a Missing-Kernel-based One-Class Support Vector Machine (MK-OCSVM), in which a novel missing kernel capable of handling missing data is constructed. MK-OCSVM avoids explicit imputation so that its anomaly detection performance is not affected by the imputation quality. Besides, we use the partially observed data in the observation space to model the data distribution in the latent original space, thereby enabling anomaly detection for the latent complete data. MK-OCSVM incorporates the data distribution modeling and OCSVM construction in a joint optimization framework, which alleviates the singularity issue and enhances the suitability of the missing kernel for anomaly detection. To solve the joint optimization problem of MK-OCSVM, a gradient-based alternating optimization algorithm is designed. Experimental results demonstrate the effectiveness of the proposed method.
Keywords:
Anomaly detection
missing data
one-class support vector machine (OCSVM)
data distribution modeling