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  Rapid Distance-Based Outlier Detection via Sampling

Sugiyama, M., & Borgwardt, K. (2013). Rapid Distance-Based Outlier Detection via Sampling. Advances in Neural Information Processing Systems 26 (NIPS 2013), 467-475.

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 Creators:
Sugiyama, Mahito, Author
Borgwardt, Karsten1, Author                 
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1Dept. Empirical Inference, Max Planck Institute for Intelligent System, Max Planck Society, ou_1497647              

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 Abstract: Distance-based approaches to outlier detection are popular in data mining, as they do not require to model the underlying probability distribution, which is particularly challenging for high-dimensional data. We present an empirical comparison of various approaches to distance-based outlier detection across a large number of datasets. We report the surprising observation that a simple, sampling-based scheme outperforms state-of-the-art techniques in terms of both efficiency and effectiveness. To better understand this phenomenon, we provide a theoretical analysis why the sampling-based approach outperforms alternative methods based on k-nearest neighbor search.

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 Dates: 20132013
 Publication Status: Issued
 Pages: 467-​475
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Title: Advances in Neural Information Processing Systems 26 (NIPS 2013)
Source Genre: Journal
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 467 - 475 Identifier: -