ODHD: One-Class Hyperdimensional Computing for Outlier Detection
TimeTuesday, July 12th11:37am - 12pm PDT
Location3000, Level 3
Event Type
Research Manuscript
AI/ML Security/Privacy
ML Algorithms and Applications
DescriptionIn this paper, we propose ODHD, an outlier detection method based on brain-inspired hyperdimensional computing (HDC). In ODHD, the outlier detection process is based on a P-U learning structure, in which we train an one-class HV using inlier samples. This HV represents the abstraction information of all inlier samples and any (testing) sample whose corresponding HV is dissimilar from this HV will be considered as an outlier. We perform an extensive evaluation using 6 datasets across different application domains and compare ODHD with 3 baseline methods including OCSVM, isolation forest, and autoencoder using 4 metrics.