  The extraction of clusters from a dataset which includes multiple clusters
and a significant background component is a non-trivial task of practical
importance. In image analysis this manifests for example in anomaly detection
and target detection. The traditional spectral clustering algorithm, which
relies on the leading $K$ eigenvectors to detect $K$ clusters, fails in such
cases. In this paper we propose the {\it spectral embedding norm} which sums
the squared values of the first $I$ normalized eigenvectors, where $I$ can be
significantly larger than $K$. We prove that this quantity can be used to
separate clusters from the background in unbalanced settings, including extreme
cases such as outlier detection. The performance of the algorithm is not
sensitive to the choice of $I$, and we demonstrate its application on synthetic
and real-world remote sensing and neuroimaging datasets.
