Outlier Detection for Text Data : An Extended Version

Understand

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.