[3D1-GS-2] Machine learning: Image recognition

[3D1-GS-2-03] Feature Selection Based on Subspace Structure for Anomaly Detection

〇Kazuki Saito¹, Katsuya Hotta¹, Yoshihiro Hagihara¹ (1. Iwate University)

Keywords:Anomaly Detection, Subspace, Machine Learning

Anomaly detection based on one-class classification, a crucial aspect in industrial manufacturing, aims to identify anomalous samples that deviate from patterns established exclusively from normal ones. Conventional methods using a memory bank generally address anomaly detection by storing normal patterns in advance. However, increasing the number of normal samples for training leads to high computational complexity. Furthermore, relying only on a random subsample can result in a weak performance when the subsample is not representative of the structure of the data. In this study, we propose a feature selection based on subspace structure for anomaly detection, which selects only the features representing normal patterns. Specifically, we capture normal patterns with a few samples by constructing the subspace structure based on Nearest Subspace Neighbor (NSN). Experimental evaluations demonstrate that our method maintains high anomaly detection performance despite employing a limited number of selected features.