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Proceedings of the 2017 SIAM International Conference on Data Mining

Outlier Detection for Text Data

Abstract

The problem of outlier detection is extremely challenging in many domains such as text, in which the attribute values are typically non-negative, and most values are zero. In such cases, it often becomes difficult to separate the outliers from the natural variations in the patterns in the underlying data. In this paper, we present a matrix factorization method, which is naturally able to distinguish the anomalies with the use of low rank approximations of the underlying data. Our iterative algorithm TONMF is based on Block Coordinate Descent (BCD) framework. Our approach has significant advantages over traditional methods for text outlier detection. Finally, we present experimental results illustrating the effectiveness of our method over competing methods.

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cover image Proceedings
Proceedings of the 2017 SIAM International Conference on Data Mining
Pages: 489 - 497
Editors: Nitesh Chawla, University of Notre Dame, Notre Dame, Indiana, USA and Wei Wang, University of California, Los Angeles, California, USA
ISBN (Online): 978-1-61197-497-3

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Published online: 9 June 2017

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