mlsanityData to 7 Oct 2026

IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation

arXiv 2108.00360 ↗First posted 1 Aug 2021cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Outlier detection is one of the most popular and continuously rising topics in the data mining field due to its crucial academic value and extensive industrial applications. Among different settings, unsupervised outlier detection is the most challenging and practical one, which attracts tremendous efforts from diverse perspectives. In this paper, we consider the score-based outlier detection category and point out that the performance of current outlier detection algorithms might be further boosted by score propagation. Specifically, we propose Infinite Propagation of Outlier Factor (iPOF) algorithm, an extremely and excitingly simple outlier detection booster via infinite propagation. By employing score-based outlier detectors for initialization, iPOF updates each data point's outlier score by averaging the outlier factors of its nearest common neighbors. Extensive experimental results on numerous datasets in various domains demonstrate the effectiveness and efficiency of iPOF significantly over several classical and recent state-of-the-art methods. We also provide the parameter analysis on the number of neighbors, the unique parameter in iPOF, and different initial outlier detectors for general validation. It is worthy to note that iPOF brings in positive improvements ranging from 2% to 46% on the average level, and in some cases, iPOF boosts the performance over 3000% over the original outlier detection algorithm.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 58 references that are in the map. First posted on arXiv 1 Aug 2021.

Builds on

17 of 58 references.

References · 17

  1. 2018Learning to Reweight Examples for Robust Deep Learningcited by 670
  2. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  3. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  4. 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
  5. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  6. 2018Efficient GAN-Based Anomaly Detectioncited by 122
  7. 2018Anomaly Detection using One-Class Neural Networkscited by 82
  8. 2018Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detectioncited by 79
  9. 2015Learning Deep Representations of Appearance and Motion for Anomalous Event Detectioncited by 75
  10. 2014Toward Supervised Anomaly Detectioncited by 73
  11. 2018Generative Adversarial Active Learning for Unsupervised Outlier Detectioncited by 65
  12. 2018Anomaly Detection with Generative Adversarial Networks for Multivariate Time Seriescited by 55
5 more
  1. 2019Unsupervised Representation Learning by Predicting Random Distancescited by 34
  2. 2018LSCP: Locally Selective Combination in Parallel Outlier Ensemblescited by 31
  3. 2018Clustering with Outlier Removalcited by 10
  4. 2017Outlier Detection for Text Data : An Extended Versioncited by 8
  5. 2018Localized Multiple Kernel Learning for Anomaly Detection: One-class Classificationcited by 5

Built on by

Not cited yet.