  The field of Movement Ecology, like so many other fields, is experiencing a
period of rapid growth in availability of data. As the volume rises,
traditional methods are giving way to machine learning and data science, which
are playing an increasingly large part it turning this data into
science-driving insights. One rich and interesting source is the bio-logger.
These small electronic wearable devices are attached to animals free to roam in
their natural habitats, and report back readings from multiple sensors,
including GPS and accelerometer bursts. A common use of accelerometer data is
for supervised learning of behavioral modes. However, we need unsupervised
analysis tools as well, in order to overcome the inherent difficulties of
obtaining a labeled dataset, which in some cases is either infeasible or does
not successfully encompass the full repertoire of behavioral modes of interest.
Here we present a matrix factorization based topic-model method for
accelerometer bursts, derived using a linear mixture property of patch
features. Our method is validated via comparison to a labeled dataset, and is
further compared to standard clustering algorithms.
