  Weird galaxies are outliers that have either unknown or very uncommon
features making them different from the normal sample. These galaxies are very
interesting as they may provide new insights into current theories, or can be
used to form new theories about processes in the Universe. Interesting outliers
are often found by accident, but this will become increasingly more difficult
with future big surveys generating an enormous amount of data. This gives the
need for machine learning detection techniques to find the interesting weird
objects. In this work, we inspect the galaxy spectra of the third data release
of the Galaxy And Mass Assembly survey and look for the weird outlying galaxies
using two different outlier detection techniques. First, we apply
distance-based Unsupervised Random Forest on the galaxy spectra using the flux
values as input features. Spectra with a high outlier score are inspected and
divided into different categories such as blends, quasi-stellar objects, and
BPT outliers. We also experiment with a reconstruction-based outlier detection
method using a variational autoencoder and compare the results of the two
different methods. At last, we apply dimensionality reduction techniques on the
output of the methods to inspect the clustering of similar spectra. We find
that both unsupervised methods extract important features from the data and can
be used to find many different types of outliers.
