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In this paper, leveraging the capabilities of neural networks for modelingthe non-linearities that exist in the data, we propose several models that canproject data into a low dimensional, discriminative, and smooth manifold. Theproposed models can transfer knowledge from the domain of known classes to anew domain where the classes are unknown. A clustering algorithm is furtherapplied in the new domain to find potentially new classes from the pool ofunlabeled data. The research problem and data for this paper originated fromthe Gravity Spy project which is a side project of Advanced LaserInterferometer Gravitational-wave Observatory (LIGO). The LIGO project aims atdetecting cosmic gravitational waves using huge detectors. However non-cosmic,non-Gaussian disturbances known as "glitches", show up in gravitational-wavedata of LIGO. This is undesirable as it creates problems for the gravitationalwave detection process. Gravity Spy aids in glitch identification with thepurpose of understanding their origin. Since new types of glitches appear overtime, 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.

Discriminative Dimensionality Reduction using Deep Neural Networks for Clustering of LIGO Data