Signature Isolation Forest

arXiv:2403.04405v1 Announce Type: cross
Abstract: Functional Isolation Forest (FIF) is a recent state-of-the-art Anomaly Detection (AD) algorithm designed for functional data. It relies on a tree partition procedure where an abnormality score is computed by projecting each curve observation on a drawn dictionary through a linear inner product. Such linear inner product and the dictionary are a priori choices that highly influence the algorithm's performances and might lead to unreliable results, particularly with complex datasets. This work addresses these challenges …

abstract algorithm anomaly anomaly detection art arxiv cs.lg data detection dictionary functional linear observation product state stat.ml through tree type