  The success of automatic classification of variable stars strongly depends on
the lightcurve representation. Usually, lightcurves are represented as a vector
of many statistical descriptors designed by astronomers called features. These
descriptors commonly demand significant computational power to calculate,
require substantial research effort to develop and do not guarantee good
performance on the final classification task. Today, lightcurve representation
is not entirely automatic; algorithms that extract lightcurve features are
designed by humans and must be manually tuned up for every survey. The vast
amounts of data that will be generated in future surveys like LSST mean
astronomers must develop analysis pipelines that are both scalable and
automated. Recently, substantial efforts have been made in the machine learning
community to develop methods that prescind from expert-designed and manually
tuned features for features that are automatically learned from data. In this
work we present what is, to our knowledge, the first unsupervised feature
learning algorithm designed for variable stars. Our method first extracts a
large number of lightcurve subsequences from a given set of photometric data,
which are then clustered to find common local patterns in the time series.
Representatives of these patterns, called exemplars, are then used to transform
lightcurves of a labeled set into a new representation that can then be used to
train an automatic classifier. The proposed algorithm learns the features from
both labeled and unlabeled lightcurves, overcoming the bias generated when the
learning process is done only with labeled data. We test our method on MACHO
and OGLE datasets; the results show that the classification performance we
achieve is as good and in some cases better than the performance achieved using
traditional features, while the computational cost is significantly lower.
