Latent outlier detection and the low precision problem

Fei Wang, Sanjay Chawla, Didi Surian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The identification of outliers is an intrinsic component of knowledge discovery. However, most outlier detection techniques operate in the observational space, which is often associated with information redundancy and noise. Also, due to the usually high dimensionality of the observational space, the anomalies detected are difficult to comprehend. In this paper we claim that algorithms for discovery of outliers in a latent space will not only lead to more accurate results but potentially provide a natural medium to explain and describe outliers. Specifically, we propose combining Non-Negative Matrix Factorization (NMF) with subspace analysis to discover and interpret outliers. We report on preliminary work towards such an approach.

Original languageEnglish
Title of host publicationProceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013
Pages46-52
Number of pages7
DOIs
Publication statusPublished - 24 Dec 2013
EventACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013 - Chicago, IL, United States
Duration: 11 Aug 201311 Aug 2013

Publication series

NameProceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013

Conference

ConferenceACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013
CountryUnited States
CityChicago, IL
Period11/8/1311/8/13

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ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

Cite this

Wang, F., Chawla, S., & Surian, D. (2013). Latent outlier detection and the low precision problem. In Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013 (pp. 46-52). (Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, ODD 2013). https://doi.org/10.1145/2500853.2500862