论文标题
使用深神经网络降低判别性维度，以聚集Ligo数据
Discriminative Dimensionality Reduction using Deep Neural Networks for Clustering of LIGO Data
论文作者
论文摘要
在本文中，利用神经网络的功能来建模数据中存在的非线性，我们提出了几个模型，这些模型可以将数据投射到低维，歧视性和平滑的歧管中。提出的模型可以将知识从已知类别的领域转移到尚不清楚的新域。新域中进一步应用了聚类算法，以从未标记的数据池中找到潜在的新类。本文的研究问题和数据起源于Gravity Spy项目，该项目是高级激光干涉仪重力波观测站（LIGO）的侧面项目。 Ligo项目旨在使用巨大的检测器检测宇宙引力波。但是，在Ligo的重力波数据中出现了非颜色的非陶瓷干扰，称为“小故障”。这是不希望的，因为它会为重力波检测过程带来问题。重力间谍有助于理解其起源的目的。由于新型的故障会随着时间的流逝而出现，因此重力间谍的目的之一是创建新的小故障类。在这项任务中，我们在本文中提供了一种方法来实现这一目标。
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.
