Metric anomaly detection via asymmetric risk minimization

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


We propose what appears to be the first anomaly detection framework that learns from positive examples only and is sensitive to substantial differences in the presentation and penalization of normal vs. anomalous points. Our framework introduces a novel type of asymmetry between how false alarms (misclassifications of a normal instance as an anomaly) and missed anomalies (misclassifications of an anomaly as normal) are penalized: whereas each false alarm incurs a unit cost, our model assumes that a high global cost is incurred if one or more anomalies are missed. We define a few natural notions of risk along with efficient minimization algorithms. Our framework is applicable to any metric space with a finite doubling dimension. We make minimalistic assumptions that naturally generalize notions such as margin in Euclidean spaces. We provide a theoretical analysis of the risk and show that under mild conditions, our classifier is asymptotically consistent. The learning algorithms we propose are computationally and statistically efficient and admit a further tradeoff between running time and precision. Some experimental results on real-world data are provided.

Original languageEnglish
Title of host publicationSimilarity-Based Pattern Recognition - First International Workshop, SIMBAD 2011, Proceedings
Number of pages14
StatePublished - 5 Oct 2011
Event1st International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2011 - Venice, Italy
Duration: 28 Sep 201130 Sep 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7005 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference1st International Workshop on Similarity-Based Pattern Recognition, SIMBAD 2011

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


Dive into the research topics of 'Metric anomaly detection via asymmetric risk minimization'. Together they form a unique fingerprint.

Cite this