  In this paper, benefiting from the strong ability of deep neural network in
estimating non-linear functions, we propose a discriminative embedding function
to be used as a feature extractor for clustering tasks. The trained embedding
function transfers knowledge from the domain of a labeled set of
morphologically-distinct images, known as classes, to a new domain within which
new classes can potentially be isolated and identified. Our target application
in this paper is the Gravity Spy Project, which is an effort to characterize
transient, non-Gaussian noise present in data from the Advanced Laser
Interferometer Gravitational-wave Observatory, or LIGO. Accumulating large,
labeled sets of noise features and identifying of new classes of noise lead to
a better understanding of their origin, which makes their removal from the data
and/or detectors possible.
