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

DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data