  A principal component analysis (PCA) of clean microcalorimeter pulse records
can be a first step beyond statistically optimal linear filtering of pulses
towards a fully non-linear analysis. For PCA to be practical on spectrometers
with hundreds of sensors, an automated identification of clean pulses is
required. Robust forms of PCA are the subject of active research in machine
learning. We examine a version known as coherence pursuit that is simple, fast,
and well matched to the automatic identification of outlier records, as needed
for microcalorimeter pulse analysis.
