Abstract: Outlier or anomaly detection is one of the research hotspots in areas such as data mining and machine learning,and researchers have proposed a variety of outlier detection methods that can be applied to problems such as intrusion detection and anomalous transaction detection.However,most outlier detection methods mainly target tabular data or time series data,etc.and cannot be directly applied to outlier document detection.Existing outlier detection methods based on proximity generally measure proximity by the distance of a document to the entire document set,failing to find outliers based on local considerations,and may not be able to characterize semantic proximity between documents using Euclidean distance.Probabilistic model-based outlier do-cument detection methods are too complex and define document outliers only globally.In response to these questions,this paper proposes a new proximity-based outlier document detection method where we measure the outlier of a document by the distance between the document and its k-nearest neighbor document.We introduce the optimal transport algorithm to calculate the distance between documents,based on the semantic information of the document obtained from word embedding vector and the topic model.The method defines document outliers from a local perspective,using document distances that reflect the semantic proxi-mity between documents.This paper conducts extensive experiments on two open source document datasets,and the results show that the proposed methods outperform the benchmark outlier document detection methods in terms of four evaluation metrics.Experiments also demonstrate the effectiveness of proposal of k-nearest neighbor based outliers and the impact of value k.

Key words: Hierarchical optimal topic transport, Optimal transport, Outlier document detection, Word mover’s distance