# Anomaly detection using principal component classifier, cutoffs selection

I am trying to implement anomaly detection using principal component classifier proposed in "A novel anomaly detection scheme based on principal component classifier" by Shyu et al.

It proposes that instead of using only the major principal components, it is better to use major as well as minor principal components. For example, the paper uses major principal components that explain 50% of the total variance and the minor components having eigenvalues less than 0.2.

What I am unclear about is the selection of hard cutoffs such as 50% and 0.2. Is there any science behind it? Can anyone please explain?

• As the authors say, they "suggest" to use these cutoffs "based on [their] experiments". However, they do not present any evidence that these cutoffs work better than others, and actually do not present any evidence that the cutoffs are needed at all either (instead of simply using Mahalanobis distance, i.e. all PCs). It looks like some more or less arbitrary rule of thumb that these particular authors came up with. Unless this paper or its authors are particularly famous, I would not spend much time thinking about it. – amoeba Aug 20 '15 at 14:16