  In this paper, leveraging the capabilities of neural networks for modeling
the non-linearities that exist in the data, we propose several models that can
project data into a low dimensional, discriminative, and smooth manifold. The
proposed models can transfer knowledge from the domain of known classes to a
new domain where the classes are unknown. A clustering algorithm is further
applied in the new domain to find potentially new classes from the pool of
unlabeled data. The research problem and data for this paper originated from
the Gravity Spy project which is a side project of Advanced Laser
Interferometer Gravitational-wave Observatory (LIGO). The LIGO project aims at
detecting cosmic gravitational waves using huge detectors. However non-cosmic,
non-Gaussian disturbances known as "glitches", show up in gravitational-wave
data of LIGO. This is undesirable as it creates problems for the gravitational
wave detection process. Gravity Spy aids in glitch identification with the
purpose of understanding their origin. Since new types of glitches appear over
time, one of the objective of Gravity Spy is to create new glitch classes.
Towards this task, we offer a methodology in this paper to accomplish this.
