A Flexible Framework for Anomaly Detection via Dimensionality Reduction

2019-09-09 · Alireza Vafaei Sadr, Bruce A. Bassett, Martin Kunz

Anomaly detection is challenging, especially for large datasets in high dimensions. Here we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. We release DRAMA, a general python package that implements the general framework with a wide range of built-in options. We test DRAMA on a wide variety of simulated and real datasets, in up to 3000 dimensions, and find it robust and highly competitive with commonly-used anomaly detection algorithms, especially in high dimensions. The flexibility of the DRAMA framework allows for significant optimization once some examples of anomalies are available, making it ideal for online anomaly detection, active learning and highly unbalanced datasets.

📄 PDF Abstract BibTeX arXiv:1909.04060

Code (1)

vafaei-ar/drama 공식 구현 tf

Tasks

Active Learning Anomaly DetectionClusteringDimensionality Reduction

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