Outlier Detection Method Based on Adaptive Graph Autoencoder

Outlier detection involves identifying a small number of individuals in a dataset that differ from the majority of samples,thereby obtaining insights into the overall health and abnormal information of the data.Currently,in the context of Euclidean structured datasets,most detection algorithms predominantly treat data as independent entities,overlooking the correlations between data instances.This informational bias hinders the effective identification of potential outliers that might exist within the normal data regions.To address this issue,this paper proposes a deep joint representation learning algorithm named adaptive neighbor graph autoencoder(ANGAE).This algorithm constructs a graph from the perspective of graph generation to capture the relationships between data points and leverages structural and attribute autoencoders to learn latent representations of the data.ANGAE introduces an adaptive neighbor graph construction mechanism to dynamically update the graph structure,ensuring the adjustment and improvement of inaccurate graph structures during model training.By integrating structural embeddings and attribute embeddings,ANGAE facilitates effective interaction between network structure and node attributes.Experimental results demonstrate that the proposed method achieves superior performance across 11 datasets,maintaining high precision while exhibiting robust resilience,thereby substantiating the method's efficacy.