Semi‐supervised Eigenbasis novelty detection

摘要

Abstract We present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses sparse, adaptive eigenbases to combine (1) prior knowledge about uninteresting signals with (2) online estimation of the current data properties to enable highly sensitive and precise detection of novel signals. We apply Semi-Supervised Eigenbasis Novelty Detection (SSEND) to the problem of detecting fast transient radio anomalies and com.