  The LIGO observatories detect gravitational waves through monitoring changes
in the detectors' length down to below $10^{-19}$\,$m/\sqrt{Hz}$ variation---a
small fraction of the size of the atoms that make up the detector. To achieve
this sensitivity, the detector and its environment need to be closely
monitored. Beyond the gravitational wave data stream, LIGO continuously records
hundreds of thousands of channels of environmental and instrumental data in
order to monitor for possibly minuscule variations that contribute to the
detector noise. A particularly challenging issue is the appearance in the
gravitational wave signal of brief, loud noise artifacts called ``glitches,''
which are environmental or instrumental in origin but can mimic true
gravitational waves and therefore hinder sensitivity. Currently they are
primarily identified by analysis of the gravitational wave data stream. Here we
present a machine learning approach that can identify glitches by monitoring
\textit{all} environmental and detector data channels, a task that has not
previously been pursued due to its scale and the number of degrees of freedom
within gravitational-wave detectors. The presented method is capable of
reducing the gravitational-wave detector network's false alarm rate and
improving the LIGO instruments, consequently enhancing detection confidence.
